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An Information-Theoretic Approach on Causal Structure Learning for Heterogeneous Data Characteristics of Real-World Scenarios
| null |
While the knowledge about the structures of a system’s underlying causal relationships is crucial within many real-world scenarios, the omnipresence of heterogeneous data characteristics impedes applying methods for causal structure learning (CSL).
In this dissertation project, we reduce the barriers for the transfer of CSL into practice with threefold contributions:
(1) We derive an information-theoretic conditional independence test that, incorporated into methods for CSL, improves the accuracy for non-linear and mixed discrete-continuous causal relationships;
(2) We develop a modular pipeline that covers the essential components required for a comprehensive benchmarking to support the transferability into practice;
(3) We evaluate opportunities and challenges of CSL within different real-world scenarios from genetics and discrete manufacturing to demonstrate the accuracy of our approach in practice.
|
Johannes Huegle
| null | null | 2,021 |
ijcai
|
Planning and Reinforcement Learning for General-Purpose Service Robots
| null |
Despite recent progress in AI and robotics research, especially learned robot skills, there remain significant challenges in building robust, scalable, and general-purpose systems for service robots. This Ph.D. research aims to combine symbolic planning and reinforcement learning to reason about high-level robot tasks and adapt to the real world. We will introduce task planning algorithms that adapt to the environment and other agents, as well as reinforcement learning methods that are practical for service robot systems. Taken together, this work will make a significant step towards creating general-purpose service robots.
|
Yuqian Jiang
| null | null | 2,021 |
ijcai
|
Safety Analysis of Deep Neural Networks
| null |
Deep Neural Networks (DNNs) are popular machine learning models which have found successful application in many different domains across computer science.
Nevertheless, providing formal guarantees on the behaviour of neural networks is hard and therefore their reliability in safety-critical domains is still a concern.
Verification and repair emerged as promising solutions to address this issue. In the following, I will present some of my recent efforts in this area.
|
Dario Guidotti
| null | null | 2,021 |
ijcai
|
Distributional Metareasoning for Heuristic Search
| null |
Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this work, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning.
|
Tianyi Gu
| null | null | 2,021 |
ijcai
|
Towards Robust Dynamic Network Embedding
| null |
Dynamic Network Embedding (DNE) has recently drawn much attention due to the dynamic nature of many real-world networks. Comparing to a static network, a dynamic network has a unique character called the degree of changes, which can be defined as the average number of the changed edges between consecutive snapshots spanning a dynamic network. The degree of changes could be quite different even for the dynamic networks generated from the same dataset. It is natural to ask whether existing DNE methods are effective and robust w.r.t. the degree of changes. Towards robust DNE, we suggest two important scenarios. One is to investigate the robustness w.r.t. different slicing settings that are used to generate different dynamic networks with different degree of changes, while another focuses more on the robustness w.r.t. different number of changed edges over timesteps.
|
Chengbin Hou, Ke Tang
| null | null | 2,021 |
ijcai
|
Nash Welfare in the Facility Location Problem
| null |
In most facility location research, either an efficient facility placement which minimizes the total cost or a fairer placement which minimizes the maximum cost are typically proposed. To find a solution that is both fair and efficient, we propose converting the agent costs to utilities and placing the facility/ies such that the product of utilities, also known as the Nash welfare, is maximized. We ask whether the Nash welfare's well-studied balance between fairness and efficiency also applies to the facility location setting, and what agent strategic behaviour may occur under this facility placement.
|
Alexander Lam
| null | null | 2,021 |
ijcai
|
AI for Planning Public Health Interventions
| null |
Several scenarios involving public health interventions have a unifying underlying theme, that deals with the challenge of optimizing the limited intervention resources available. My dissertation casts this as a Restless Multi-Armed Bandit (RMAB) planning problem, identifying and addressing several new, fundamental questions in RMABs.
|
Aditya Mate
| null | null | 2,021 |
ijcai
|
Deep Reinforcement Learning with Hierarchical Structures
| null |
Hierarchical reinforcement learning (HRL), which enables control at multiple time scales, is a promising paradigm to solve challenging and long-horizon tasks. In this paper, we briefly introduce our work in bottom-up and top-down HRL and outline the directions for future work.
|
Siyuan Li
| null | null | 2,021 |
ijcai
|
Towards an Explainer-agnostic Conversational XAI
| null |
Explainable Artificial Intelligence (XAI) is gaining interests in both academia and industry, mainly thanks to the proliferation of darker more complex black-box solutions which are replacing their more transparent ancestors. Believing that the overall performance of an XAI system can be augmented by considering the end-user as a human being, we are studying the ways we can improve the explanations by making them more informative and easier to use from one hand, and interactive and customisable from the other hand.
|
Navid Nobani, Fabio Mercorio, Mario Mezzanzanica
| null | null | 2,021 |
ijcai
|
Automated Facilitation Support in Online Forum
| null |
Online forum that gathers participants together to solve the common issues that they are facing is considered as a promising application of utilizing collective intelligence to solve complicated real-world problems.
To facilitate the discussions in online forum to proceed smoothly and to build consensus efficiently, human facilitators are introduced into the system.
With the increasing sophistication of online forum, human facilitators related problems such as human bias and restricted scale become critical.
Therefore, it is critical to explore approaches to support human facilitators in conducting facilitation.
However, most of the existing facilitation support techniques only support predefined facilitation tasks that could be defined by static rules.
In this research, we aim to explore potential solutions for supporting the human facilitators to conduct facilitation in online forum.
As the first step, we have proposed a case-based reasoning (CBR)-based framework that targets support facilitation by utilizing past successful facilitation experience.
Currently, our work is focusing on the specific facilitation task of detecting influential user in the online forum.
In the future work, we are planning to propose approaches of solving other specific facilitation tasks such as measuring the level of agreement and encouraging participants to reach a consensus.
|
Wen Gu
| null | null | 2,021 |
ijcai
|
Beyond Accuracy: Behavioral Testing of NLP Models with Checklist (Extended Abstract)
| null |
Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors. Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly. We illustrate the utility of CheckList with tests for three tasks, identifying critical failures in both commercial and state-of-art models. In a user study, a team responsible for a commercial sentiment analysis model found new and actionable bugs in an extensively tested model. In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.
|
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, Sameer Singh
| null | null | 2,021 |
ijcai
|
Uncertain Time Series Classification
| null |
Time series analysis has gained a lot of interest during the last decade with diverse applications in a large range of domains such as medicine, physic, and industry. The field of time series classification has been particularly active recently with the development of more and more efficient methods. However, the existing methods assume that the input time series is free of uncertainty. However, there are applications in which uncertainty is so important that it can not be neglected. This project aims to build efficient, robust, and interpretable classification methods for uncertain time series.
|
Michael Franklin Mbouopda
| null | null | 2,021 |
ijcai
|
On the Learnability of Knowledge in Multi-Agent Logics
| null |
Since knowledge engineering is an inherently challenging and somewhat unbounded task, machine learning has been widely proposed as an alternative. In real world scenarios, we often need to explicitly model multiple agents, where intelligent agents act towards achieving goals either by coordinating with the other agents or by overseeing the opponents moves, if in a competitive context. We consider the knowledge acquisition problem where agents have knowledge about the world and other agents and then acquire new knowledge (both about the world as well as other agents) in service of answering queries. We propose a model of implicit learning, or more generally, learning to reason, which bypasses the intractable step of producing an explicit representation of the learned knowledge. We show that polynomial-time learnability results can be obtained when limited to knowledge bases and observations consisting of conjunctions of modal literals.
|
Ionela G Mocanu
| null | null | 2,021 |
ijcai
|
A Human-AI Teaming Approach for Incremental Taxonomy Learning from Text
| null |
Taxonomies provide a structured representation of semantic relations between lexical terms, acting as the backbone of many applications. The research proposed herein addresses the topic of taxonomy enrichment using an ”human-in-the-loop” semi-supervised approach. I will be investigating possible ways to extend and enrich a taxonomy using corpora of unstructured text data. The objective is to develop a methodological framework potentially applicable to any domain.
|
Andrea Seveso, Fabio Mercorio, Mario Mezzanzanica
| null | null | 2,021 |
ijcai
|
Inter-Task Similarity for Lifelong Reinforcement Learning in Heterogeneous Tasks
| null |
Reinforcement learning (RL) is a learning paradigm in which an agent interacts with the environment it inhabits to learn in a trial-and-error way. By letting the agent acquire knowledge from its own experience, RL has been successfully applied to complex domains such as robotics. However, for non-trivial problems, training an RL agent can take very long periods of time. Lifelong machine learning (LML) is a learning setting in which the agent learns to solve tasks sequentially, by leveraging knowledge accumulated from previously solved tasks to learn better/faster in a new one. Most LML works heavily rely on the assumption that tasks are similar to each other. However, this may not be true for some domains with a high degree of task-diversity that could benefit from adopting a lifelong learning approach, e.g., service robotics. Therefore, in this research we will address the problem of learning to solve a sequence of RL heterogeneous tasks (i.e., tasks that differ in their state-action space).
|
Sergio A. Serrano
| null | null | 2,021 |
ijcai
|
Learning from Multimedia Data with Incomplete Information
| null |
Traditional deep learning methods are based on the condition that the data is of high-quality, which means the data information is highly available. However, data in these scenes often have the characteristics of large background noise, lack of sample content, small target, serious occlusion and a small number of samples. The application of related tasks in real open scenarios is very important, so it is urgent to make full use of these incomplete information data accurately.
|
Renshuai Tao
| null | null | 2,021 |
ijcai
|
An Automated Framework for Supporting Data-Governance Rule Compliance in Decentralized MIMO Contexts
| null |
We propose Dr.Aid, a logic-based AI framework for automated compliance checking of data governance rules over data-flow graphs. The rules are modelled using a formal language based on situation calculus and are suitable for decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid models data rules and flow rules and checks compliance by reasoning about the propagation, combination, modification and application of data rules over the data flow graphs. Our approach is driven and evaluated by real-world datasets using provenance graphs from data-intensive research.
|
Rui Zhao
| null | null | 2,021 |
ijcai
|
Modeling Institutions in Socio-Ecosystems
| null |
In socio-ecosystems, human activities are structured in time and space by interactions between different regulatory systems with different collective goals. These regulatory systems are modeled by institutions and organizations, and the regulatory mechanisms by norms applied to agents in Multi-Agent Systems (MAS). However, little is said about sharing resources, space and time. In particular, temporal and spatial expressivity is often limited in MAS for institutions and norms. This research proposes an institutional MAS model capable of representing multiple institutions and norms in the socio-ecosystem, in order to account for the multiplicity of interactions through agents, resources, space and time. We propose an extension of Descriptive Logic for the description of institutions and norms, and use Allen's algebra and the RCC8 to represent time and space. The resulting model allows us to know the norms applicable to an agent located socially, spatially and temporally.
|
Sitraka Oliva Raharivelo, Jean-Pierre Müller
| null | null | 2,021 |
ijcai
|
Continual Lifelong Learning for Intelligent Agents
| null |
Deep neural networks have achieved outstanding performance in many machine learning tasks. However, this remarkable success is achieved in a closed and static environment where the model is trained using large training data of a single task and deployed for testing on data with a similar distribution. Once the model is deployed, it becomes fixed and inflexible to new knowledge. This contradicts real-world applications, in which agents interact with open and dynamic environments and deal with non-stationary data. This Ph.D. research aims to propose efficient approaches that can develop intelligent agents capable of accumulating new knowledge and adapting to new environments without forgetting the previously learned ones.
|
Ghada Sokar
| null | null | 2,021 |
ijcai
|
Combining Reinforcement Learning and Causal Models for Robotics Applications
| null |
The relation between Reinforcement learning (RL) and Causal Modeling(CM) is an underexplored area with untapped potential for any learning task. In this extended abstract of our Ph.D. research proposal, we present a way to combine both areas to improve their respective learning processes, especially in the context of our application area (service robotics). The preliminary results obtained so far are a good starting point for thinking about the success of our research project.
|
Arquímides Méndez-Molina
| null | null | 2,021 |
ijcai
|
Learning and Planning Under Uncertainty for Green Security
| null |
Green security concerns the protection of the world's wildlife, forests, and fisheries from poaching, illegal logging, and illegal fishing. Unfortunately, conservation efforts in green security domains are constrained by the limited availability of defenders, who must patrol vast areas to protect from attackers. Artificial intelligence (AI) techniques have been developed for green security and other security settings, such as US Coast Guard patrols and airport screenings, but effective deployment of AI in these settings requires learning adversarial behavior and planning in complex environments where the true dynamics may be unknown. My research develops novel techniques in machine learning and game theory to enable the effective development and deployment of AI in these resource-constrained settings. Notably, my work has spanned the pipeline from learning in a supervised setting, planning in stochastic environments, sequential planning in uncertain environments, and deployment in the real world. The overarching goal is to optimally allocate scarce resources under uncertainty for environmental conservation.
|
Lily Xu
| null | null | 2,021 |
ijcai
|
Adaptive Experimental Design for Optimizing Combinatorial Structures
| null |
Scientists and engineers in diverse domains need to perform expensive experiments to optimize combinatorial spaces, where each candidate input is a discrete structure (e.g., sequence, tree, graph) or a hybrid structure (mixture of discrete and continuous design variables). For example, in hardware design optimization over locations of processing cores and communication links for data transfer, design evaluation involves performing a computationally-expensive simulation. These experiments are often performed in a heuristic manner by humans and without any formal reasoning. In this paper, we first describe the key challenges in solving these problems in the framework of Bayesian optimization (BO) and our progress over the last five years in addressing these challenges. We also discuss exciting sustainability applications in domains such as electronic design automation, nanoporous materials science, biological sequence design, and electric transportation systems.
|
Janardhan Rao Doppa
| null | null | 2,021 |
ijcai
|
Anomaly Mining - Past, Present and Future
| null |
Anomaly mining is an important problem that finds numerous applications in various real world do- mains such as environmental monitoring, cybersecurity, finance, healthcare and medicine, to name a few. In this article, I focus on two areas, (1) point-cloud and (2) graph-based anomaly mining. I aim to present a broad view of each area, and discuss classes of main research problems, recent trends and future directions. I conclude with key take-aways and overarching open problems.
Disclaimer. I try to provide an overview of past and recent trends in both areas within 4 pages. Undoubtedly, these are my personal view of the trends, which can be organized differently. For brevity, I omit all technical details and refer to corresponding papers. Again, due to space limit, it is not possible to include all (even most relevant) references, but a few representative examples.
|
Leman Akoglu
| null | null | 2,021 |
ijcai
|
From Computational Social Choice to Digital Democracy
| null |
Digital Democracy (aka e-democracy or interactive democracy) aims to enhance democratic decision-making processes by utilizing digital technology. A common goal of these approaches is to make collective decision-making more engaging, inclusive, and responsive to participants' opinions. For example, online decision-making platforms often provide much more flexibility and interaction possibilities than traditional democratic systems. It is without doubt that the successful design of digital democracy systems presents a multidisciplinary research challenge. I argue that tools and techniques from computational social choice should be employed to aid the design of online decision-making platforms and other digital democracy systems.
|
Markus Brill
| null | null | 2,021 |
ijcai
|
Data Efficient Algorithms and Interpretability Requirements for Personalized Assessment of Taskable AI Systems
| null |
The vast diversity of internal designs of black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. The focus of my dissertation is to develop algorithms and requirements of interpretability that would enable a user to assess and understand the limits of an AI system's safe operability. We develop an assessment module that lets an AI system execute high-level instruction sequences in simulators and answer the user queries about its execution of sequences of actions. Our results show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable model of the system in stationary, fully observable, and deterministic settings.
|
Pulkit Verma
| null | null | 2,021 |
ijcai
|
Adversarial Examples in Physical World
| null |
Although deep neural networks (DNNs) have already made fairly high achievements and a very wide range of impact, their vulnerability attracts lots of interest of researchers towards related studies about artificial intelligence (AI) safety and robustness this year. A series of works reveals that the current DNNs are always misled by elaborately designed adversarial examples. And unfortunately, this peculiarity also affects real-world AI applications and places them at potential risk. we are more interested in physical attacks due to their implementability in the real world. The study of physical attacks can effectively promote the application of AI techniques, which is of great significance to the security development of AI.
|
Jiakai Wang
| null | null | 2,021 |
ijcai
|
Intelligent and Learning Agents: Four Investigations
| null |
My research is driven by my curiosity about the nature of intelligence. Of the several aspects that characterise the behaviour of intelligent agents, I primarily study sequential decision making, learning, and exploration. My interests also extend to broader questions on the effects of AI on life and society. In this paper, I present four distinct investigations drawn from my recent work, which range from theoretical to applied, and which involve both analysis and design. I also share my outlook as an early-career researcher.
|
Shivaram Kalyanakrishnan
| null | null | 2,021 |
ijcai
|
Alleviating Road Traffic Congestion with Artificial Intelligence
| null |
This paper reviews current AI solutions towards road traffic congestion alleviation. Three specific AI technologies are discussed, (1) intersection management protocols for coordinating vehicles through a roads intersection in a safe and efficient manner, (2) road pricing protocol that induce optimized traffic flow, and (3) partial or full autonomous driving that can stabilize traffic flow and mitigate adverse traffic shock waves. The paper briefly presents the challenges affiliated with each of these applications along with an overview of state-of-the-art solutions. Finally, real-world implementation gaps and challenges are discussed.
|
Guni Sharon
| null | null | 2,021 |
ijcai
|
HIVE: Hierarchical Information Visualization for Explainability
| null |
In this demonstration, we develop an interactive tool, HIVE, to demonstrate the ability and versatility of an explainable risk ranking model with a special focus on financial use cases. HIVE is a web-based tool that provides users with automated highlighted financial statements, and HIVE is designed for making comparing statements rather more efficient. Moreover, with the proposed tool, users can find related reports at ease, and we believe that HIVE can benefit both academics and practitioners in finance as they can work around deep learning models with their newly gained insights.
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Yi-Ning Juan, Yi-Shyuan Chiang, Shang-Chuan Liu, Ming-Feng Tsai, Chuan-Ju Wang
| null | null | 2,021 |
ijcai
|
Predictive Analytics for COVID-19 Social Distancing
| null |
The COVID-19 pandemic has disrupted the lives of millions across the globe. In Singapore, promoting safe distancing by managing crowds in public areas have been the cornerstone of containing the community spread of the virus. One of the most important solutions to maintain social distancing is to monitor the crowdedness of indoor and outdoor points of interest. Using Nanyang Technological University (NTU) as a testbed, we develop and deploy a platform that provides live and predicted crowd counts for key locations on campus to help users plan their trips in an informed manner, so as to mitigate the risk of community transmission.
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Harold Ze Chie Teng, Hongchao Jiang, Xuan Rong Zane Ho, Wei Yang Bryan Lim, Jer Shyuan Ng, Han Yu, Zehui Xiong, Dusit Niyato, Chunyan Miao
| null | null | 2,021 |
ijcai
|
A Compression-Compilation Framework for On-mobile Real-time BERT Applications
| null |
Transformer-based deep learning models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model meets both resource and real-time specifications of mobile devices. Our framework applies a compiler-aware neural architecture optimization method (CANAO), which can generate the optimal compressed model that balances both accuracy and latency. We are able to achieve up to 7.8x speedup compared with TensorFlow-Lite with only minor accuracy loss. We present two types of BERT applications on mobile devices: Question Answering (QA) and Text Generation. Both can be executed in real-time with latency as low as 45ms. Videos for demonstrating the framework can be found on https://www.youtube.com/watch?v=_WIRvK_2PZI
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Wei Niu, Zhenglun Kong, Geng Yuan, Weiwen Jiang, Jiexiong Guan, Caiwen Ding, Pu Zhao, Sijia Liu, Bin Ren, Yanzhi Wang
| null | null | 2,021 |
ijcai
|
Towards Fair and Transparent Algorithmic Systems
| null |
My research in the past few years has focused on fostering trust in algorithmic systems. I often analyze scenarios where a variety of desirable trust-oriented goals must be simultaneously satisfied;
for example, ensuring that an allocation mechanism is both fair and efficient, or that a model explanation framework is both effective and differentially private.
This interdisciplinary approach requires tools from a variety of computer science disciplines, such as game theory, economics, ML and differential privacy.
|
Yair Zick
| null | null | 2,021 |
ijcai
|
Safe Weakly Supervised Learning
| null |
Weakly supervised learning (WSL) refers to learning from a large amount of weak supervision data. This includes i) incomplete supervision (e.g., semi-supervised learning); ii) inexact supervision (e.g., multi-instance learning) and iii) inaccurate supervision (e.g., label noise learning). Unlike supervised learning which typically achieves performance improvement with more labeled data, WSL may sometimes even degenerate performance with more weak supervision data. It is thus desired to study safe WSL, which could robustly improve performance with weak supervision data. In this article, we share our understanding of the problem from in-distribution data to out-of-distribution data, and discuss possible ways to alleviate it, from the aspects of worst-case analysis, ensemble-learning, and bi-level optimization. We also share some open problems, to inspire future researches.
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Yu-Feng Li
| null | null | 2,021 |
ijcai
|
A Neural Network Auction For Group Decision Making Over a Continuous Space
| null |
We propose a system for conducting an auction over locations in a continuous space. It enables participants to express their preferences over possible choices of location in the space, selecting the location that maximizes the total utility of all agents. We prevent agents from tricking the system into selecting a location that improves their individual utility at the expense of others by using a pricing rule that gives agents no incentive to misreport their true preferences.
The system queries participants for their utility in many random locations, then trains a neural network to approximate the preference function of each participant. The parameters of these neural network models are transmitted and processed by the auction mechanism, which composes these into differentiable models that are optimized through gradient ascent to compute the final chosen location and charged prices.
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Yoram Bachrach, Ian Gemp, Marta Garnelo, Janos Kramar, Tom Eccles, Dan Rosenbaum, Thore Graepel
| null | null | 2,021 |
ijcai
|
Skills2Graph: Processing million Job Ads to face the Job Skill Mismatch Problem
| null |
In this paper, we present Skills2Graph, a tool that, starting from a set of users’ professional skills, identifies the most suitable jobs as they emerge from a large corpus of 2.5M+ Online Job Vacancies (OJVs) posted in three different countries (the United Kingdom, France, and Germany). To this aim, we rely both on co-occurrence statistics - computing a count-based measure of skill-relevance named Revealed Comparative Advantage (rca) - and distributional semantics - generating several embeddings on the OJVs corpus and performing an intrinsic evaluation of their quality. Results, evaluated through a user study of 10 labor market experts, show a high P@3 for the recommendations provided by Skills2Graph, and a high nDCG (0.985 and 0.984 in a [0,1] range), that indicates a strong correlation between the experts’ scores and the rankings generated by Skills2Graph.
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Anna Giabelli, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Andrea Seveso
| null | null | 2,021 |
ijcai
|
ConvLogMiner: A Real-Time Conversational Lifelog Miner
| null |
This paper presents a conversational lifelog mining system, ConvLogMiner, which detects personal life events from the human online conversation in real-time. Given a daily conversation of two speakers, ConvLogMiner identifies the new life events specific to each speaker that occur in the latest utterances. The lifelogs mined by our system are useful to provide complementary information to support lifestyle analysis and memory assistance service.
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Pei-Wei Kao, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen
| null | null | 2,021 |
ijcai
|
Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning
| null |
Width-based algorithms search for solutions through a general definition of state novelty. These algorithms have been shown to result in state-of-the-art performance in classical planning, and have been successfully applied to model-based and model-free settings where the dynamics of the problem are given through simulation engines. Width-based algorithms performance is understood theoretically through the notion of planning width, providing polynomial guarantees on their runtime and memory consumption. To facilitate synergies across research communities, this paper summarizes the area of width-based planning, and surveys current and future research directions.
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Nir Lipovetzky
| null | null | 2,021 |
ijcai
|
Connect Multi-Agent Path Finding: Generation and Visualization
| null |
We present a generic tool to visualize missions of the Connected Multi-Agent Path Finding (CMAPF) problem. This problem is a variant of MAPF which requires a group of agents to navigate from an initial configuration to a goal configuration while maintaining connection. The user can create an instance of CMAPF and can play the generated plan. Any algorithm for CMAPF can be plugged into the tool.
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Arthur Queffelec, Ocan Sankur, Francois Schwarzentruber
| null | null | 2,021 |
ijcai
|
Web Interoperability for Ontology Development and Support with crowd 2.0
| null |
In this work, we treat web interoperability in terms of interchanging ontologies (as knowledge models) within user-centred ontology engineering environments, involving visual and serialised representations of ontologies.
To do this, we deal with the tool interoperability problem by re-using an enough expressive ontology-driven metamodel, named KF, proposed as a bridge for interchanging both knowledge models.
We provide an extensible web framework, named crowd 2.0, unifying the standard conceptual data modelling languages for generating OWL 2 ontologies from semantic visualisations. Visual models are designed as UML, ER or ORM 2 diagrams, represented as KF instances, and finally, formalised as DL-based models. Reasoning results may be newly incorporated into the shared KF instance to be visualised in any of the provided languages.
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German Braun, Giuliano Marinelli, Emiliano Rios Gavagnin, Laura Cecchi, Pablo Fillottrani
| null | null | 2,021 |
ijcai
|
InfOCF-Web: An Online Tool for Nonmonotonic Reasoning with Conditionals and Ranking Functions
| null |
InfOCF-Web provides implementations of system P and system Z inference, and of inference relations based on c-representation with respect to various inference modes and different classes of minimal models. It has an easy-to-use online interface for computing ranking models of a conditional knowledge R, and for answering queries and comparing inference results of nonmonotonic inference relations induced by R.
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Steven Kutsch, Christoph Beierle
| null | null | 2,021 |
ijcai
|
Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices
| null |
The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, especially on mobile platforms. However, to achieve high accuracy, state-of-the-art methods tend to have a large model size and complex post-processing algorithm, which costs intense computation and long end-to-end latency. To solve this problem, we propose an architecture optimization and weight pruning framework to accelerate inference of multi-person pose estimation on mobile devices. With our optimization framework, we achieve up to 2.51X faster model inference speed with higher accuracy compared to representative lightweight multi-person pose estimator.
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Xuan Shen, Geng Yuan, Wei Niu, Xiaolong Ma, Jiexiong Guan, Zhengang Li, Bin Ren, Yanzhi Wang
| null | null | 2,021 |
ijcai
|
Towards a New Generation of Cognitive Diagnosis
| null |
Cognitive diagnosis is a type of assessment for automatically measuring individuals' proficiency profiles from their observed behaviors, e.g. quantifying the mastery level of examinees on specific knowledge concepts/skills. As one of the fundamental research tasks in domains like intelligent education, a number of Cognitive Diagnosis Models (CDMs) have been developed in the past decades. Though these solutions are usually well designed based on psychometric theories, they still suffer from the limited ability of the handcrafted diagnosis functions, especially when dealing with heterogeneous data. In this paper, I will share my personal understanding of cognitive diagnosis and review our recent developments of CDMs mostly from a machine learning perspective. Meanwhile, I will show the wide applications of cognitive diagnosis.
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Qi Liu
| null | null | 2,021 |
ijcai
|
Communication-efficient and Scalable Decentralized Federated Edge Learning
| null |
Federated Edge Learning (FEL) is a distributed Machine Learning (ML) framework for collaborative training on edge devices. FEL improves data privacy over traditional centralized ML model training by keeping data on the devices and only sending local model updates to a central coordinator for aggregation. However, challenges still remain in existing FEL architectures where there is high communication overhead between edge devices and the coordinator. In this paper, we present a working prototype of blockchain-empowered and communication-efficient FEL framework, which enhances the security and scalability towards large-scale implementation of FEL.
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Austine Zong Han Yapp, Hong Soo Nicholas Koh, Yan Ting Lai, Jiawen Kang, Xuandi Li, Jer Shyuan Ng, Hongchao Jiang, Wei Yang Bryan Lim, Zehui Xiong, Dusit Niyato
| null | null | 2,021 |
ijcai
|
IIAS: An Intelligent Insurance Assessment System through Online Real-time Conversation Analysis
| null |
With the development of Chinese medical insurance industry, the amount of claim cases is growing rapidly. Ultimately, more claims necessarily indicate that the insurance company has to spend much time assessing claims and decides how much compensation the claimant should receive, which is a highly professional process that involves many complex operations. Therefore, the insurance assessor's role is essential. However, for the junior assessor often lacking in practical experience, it is not easy to quickly handle such an online procedure. In order to alleviate assessors' cognitive workload, we propose an Intelligent Insurance Assessment System (IIAS) that helps effectively collect claimant information through online real-time conversation analysis. With the assistance of IIAS, the average time cost of the insurance assessment procedure is reduced from 55 minutes to 35 minutes.
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Mengdi Zhou, Shuang Peng, Minghui Yang, Nan Li, Hongbin Wang, Li Qiao, Haitao Mi, Zujie Wen, Teng Xu, Lei Liu
| null | null | 2,021 |
ijcai
|
Mixed Strategies for Security Games with General Defending Requirements
| null |
The Stackelberg security game is played between a defender and an attacker, where the defender needs to allocate a limited amount of resources to multiple targets in order to minimize the loss due to adversarial attack by the attacker. While allowing targets to have different values, classic settings often assume uniform requirements to defend the targets. This enables existing results that study mixed strategies (randomized allocation algorithms) to adopt a compact representation of the mixed strategies.
In this work, we initiate the study of mixed strategies for the security games in which the targets can have different defending requirements. In contrast to the case of uniform defending requirement, for which an optimal mixed strategy can be computed efficiently, we show that computing the optimal mixed strategy is NP-hard for the general defending requirements setting. However, we show that strong upper and lower bounds for the optimal mixed strategy defending result can be derived. We propose an efficient close-to-optimal Patching algorithm that computes mixed strategies that use only few pure strategies. We also study the setting when the game is played on a network and resource sharing is enabled between neighboring targets. Our experimental results demonstrate the effectiveness of our algorithm in several large real-world datasets.
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Rufan Bai, Haoxing Lin, Xinyu Yang, Xiaowei Wu, Minming Li, Weijia Jia
| null | null | 2,022 |
ijcai
|
Interactive Video Acquisition and Learning System for Motor Assessment of Parkinson's Disease
| null |
Diagnosis and treatment for Parkinson's disease rely on the evaluation of motor functions, which is expensive and time consuming when performing at clinics. It is also difficult for patients to record correct movements at home without the guidance from experienced physicians. To help patients with Parkinson’s disease get better evaluation from in-home recorded movement videos, we developed an interactive video acquisition and learning system for clinical motor assessments. The system provides real-time guidance with multi-level body keypoint tracking and analysis to patients, which guarantees correct understanding and performing of clinical tasks. We tested its effectiveness on healthy subjects, and the efficiency and usability on patient groups. Experiments showed that our system enabled high quality video recordings following clinical standards, benefiting both patients and physicians. Our system provides a novel learning-based telemedicine approach for the care of patients with Parkinson’s disease.
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Yunyue Wei, Bingquan Zhu, Chen Hou, Chen Zhang, Yanan Sui
| null | null | 2,021 |
ijcai
|
An EF2X Allocation Protocol for Restricted Additive Valuations
| null |
We study the problem of fairly allocating a set of indivisible goods to a set of n agents. Envy-freeness up to any good (EFX) criterion (which
requires that no agent prefers the bundle of another agent after the removal of any single good) is known to be a remarkable analogue of envy-freeness when the resource is a set of indivisible goods.
In this paper, we investigate EFX for restricted additive valuations, that is, every good has a non-negative value, and every agent is interested in only some of the goods.
We introduce a natural relaxation of EFX called EFkX which requires that no agent envies another agent after the removal of any k goods. Our main contribution is an algorithm that finds a complete (i.e., no good is discarded) EF2X allocation for restricted additive valuations. In our algorithm we devise new concepts, namely configuration and envy-elimination that might be of independent interest.
We also use our new tools to find an EFX allocation for restricted additive valuations that discards at most n/2 -1 goods.
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Hannaneh Akrami, Rojin Rezvan, Masoud Seddighin
| null | null | 2,022 |
ijcai
|
AutoBandit: A Meta Bandit Online Learning System
| null |
Recently online multi-armed bandit (MAB) is growing rapidly, as novel problem settings and algorithms motivated by various practical applications are being studied, building on the top of the classic bandit problem. However, identifying the best bandit algorithm from lots of potential candidates for a given application is not only time-consuming but also relying on human expertise, which hinders the practicality of MAB. To alleviate this problem, this paper outlines an intelligent system called AutoBandit, equipped with many out-of-the-box MAB algorithms, for automatically and adaptively choosing the best with suitable hyper-parameters online. It is effective to help a growing application for continuously maximizing cumulative rewards of its whole life-cycle. With a flexible architecture and user-friendly web-based interfaces, it is very convenient for the user to integrate and monitor online bandits in a business system. At the time of publication, AutoBandit has been deployed for various industrial applications.
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Miao Xie, Wotao Yin, Huan Xu
| null | null | 2,021 |
ijcai
|
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search
| null |
This paper considers the capacity expansion problem in two-sided matchings, where the policymaker is allowed to allocate some extra seats as well as the standard seats. In medical residency match, each hospital accepts a limited number of doctors. Such capacity constraints are typically given in advance. However, such exogenous constraints can compromise the welfare of the doctors; some popular hospitals inevitably dismiss some of their favorite doctors. Meanwhile, it is often the case that the hospitals are also benefited to accept a few extra doctors. To tackle the problem, we propose an anytime method that the upper confidence tree searches the space of capacity expansions, each of which has a resident-optimal stable assignment that the deferred acceptance method finds. Constructing a good search tree representation significantly boosts the performance of the proposed method. Our simulation shows that the proposed method identifies an almost optimal capacity expansion with a significantly smaller computational budget than exact methods based on mixed-integer programming.
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Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki
| null | null | 2,022 |
ijcai
|
Graph-Augmented Code Summarization in Computational Notebooks
| null |
Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code and neglect the creation of the documentation in a notebook. In this work, we present a human-centered automation system, Themisto, that can support users to easily create documentation via three approaches: 1) We have developed and reported a GNN-augmented code documentation generation algorithm in a previous paper, which can generate documentation for a given source code; 2) Themisto also implements a query-based approach to retrieve the online API documentation as the summary for certain types of source code; 3) Lastly, Themistoalso enables a user prompt approach to motivate users to write documentation for some use cases that automation does not work well.
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April Wang, Dakuo Wang, Xuye Liu, Lingfei Wu
| null | null | 2,021 |
ijcai
|
Envy-Free and Pareto-Optimal Allocations for Agents with Asymmetric Random Valuations
| null |
We study the problem of allocating m indivisible items to n agents with additive utilities. It is desirable for the allocation to be both fair and efficient, which we formalize through the notions of envy-freeness and Pareto-optimality. While envy-free and Pareto-optimal allocations may not exist for arbitrary utility profiles, previous work has shown that such allocations exist with high probability assuming that all agents’ values for all items are independently drawn from a common distribution. In this paper, we consider a generalization of this model where each agent’s utilities are drawn independently from a distribution specific to the agent. We show that envy-free and Pareto-optimal allocations are likely to exist in this asymmetric model when m=Ω(n log n), which is tight up to a log log gap that also remains open in the symmetric subsetting. Furthermore, these guarantees can be achieved by a polynomial-time algorithm.
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Yushi Bai, Paul Gölz
| null | null | 2,022 |
ijcai
|
Socially Intelligent Genetic Agents for the Emergence of Explicit Norms
| null |
Norms help regulate a society. Norms may be explicit (represented in structured form) or implicit.
We address the emergence of explicit norms by developing agents who provide and reason about explanations for norm violations in deciding sanctions and identifying alternative norms. These agents use a genetic algorithm to produce norms and reinforcement learning to learn the values of these norms.
We find that applying explanations leads to norms that provide better cohesion and goal satisfaction for the agents. Our results are stable for societies with differing attitudes of generosity.
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Rishabh Agrawal, Nirav Ajmeri, Munindar Singh
| null | null | 2,022 |
ijcai
|
Better Collective Decisions via Uncertainty Reduction
| null |
We consider an agent community wishing to decide on several binary issues by means of issue-by-issue majority voting. For each issue and each agent, one of the two options is better than the other. However, some of the agents may be confused about some of the issues, in which case they may vote for the option that is objectively worse for them. A benevolent external party wants to help the agents to make better decisions, i.e., select the majority-preferred option for as many issues as possible. This party may have one of the following tools at its disposal: (1) educating some of the agents, so as to enable them to vote correctly on all issues,
(2) appointing a subset of highly competent agents to make decisions on behalf of the entire group, or (3) guiding the agents on how to delegate their votes to other agents, in a way that is consistent with the agents' opinions. For each of these tools, we study the complexity of the decision problem faced by this external party, obtaining both NP-hardness results and fixed-parameter tractability results.
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Shiri Alouf-Heffetz, Laurent Bulteau, Edith Elkind, Nimrod Talmon, Nicholas Teh
| null | null | 2,022 |
ijcai
|
Achieving Envy-Freeness with Limited Subsidies under Dichotomous Valuations
| null |
We study the problem of allocating indivisible goods among agents in a fair manner. While envy-free allocations of indivisible goods are not guaranteed to exist, envy-freeness can be achieved by additionally providing some subsidy to the agents. These subsidies can be alternatively viewed as a divisible good (money) that is fractionally assigned among the agents to realize an envy-free outcome. In this setup, we bound the subsidy required to attain envy-freeness among agents with dichotomous valuations, i.e., among agents whose marginal value for any good is either zero or one.
We prove that, under dichotomous valuations, there exists an allocation that achieves envy-freeness with a per-agent subsidy of either 0 or 1. Furthermore, such an envy-free solution can be computed efficiently in the standard value-oracle model. Notably, our results hold for general dichotomous valuations and, in particular, do not require the (dichotomous) valuations to be additive, submodular, or even subadditive. Also, our subsidy bounds are tight and provide a linear (in the number of agents) factor improvement over the bounds known for general monotone valuations.
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Siddharth Barman, Anand Krishna, Yadati Narahari, Soumyarup Sadhukhan
| null | null | 2,022 |
ijcai
|
Transparency, Detection and Imitation in Strategic Classification
| null |
Given the ubiquity of AI-based decisions that affect individuals’ lives, providing transparent explanations about algorithms is ethically sound and often legally mandatory. How do individuals strategically adapt following explanations? What are the consequences of adaptation for algorithmic accuracy? We simulate the interplay between explanations shared by an Institution (e.g. a bank) and the dynamics of strategic adaptation by Individuals reacting to such feedback. Our model identifies key aspects related to strategic adaptation and the challenges that an institution could face as it attempts to provide explanations. Resorting to an agent-based approach, our model scrutinizes: i) the impact of transparency in explanations, ii) the interaction between faking behavior and detection capacity and iii) the role of behavior imitation. We find that the risks of transparent explanations are alleviated if effective methods to detect faking behaviors are in place. Furthermore, we observe that behavioral imitation --- as often happens across societies --- can alleviate malicious adaptation and contribute to accuracy, even after transparent explanations.
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Flavia Barsotti, Ruya Gokhan Kocer, Fernando P. Santos
| null | null | 2,022 |
ijcai
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How Should We Vote? A Comparison of Voting Systems within Social Networks
| null |
Voting is a crucial methodology for eliciting and combining agents' preferences and information across many applications. Just as there are numerous voting rules exhibiting different properties, we also see many different voting systems. In this paper we investigate how different voting systems perform as a function of the characteristics of the underlying voting population and social network. In particular, we compare direct democracy, liquid democracy, and sortition in a ground truth voting context.
Through simulations -- using both real and artificially generated social networks -- we illustrate how voter competency distributions and levels of direct participation affect group accuracy differently in each voting mechanism. Our results can be used to guide the selection of a suitable voting system based on the characteristics of a particular voting setting.
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Shiri Alouf-Heffetz, Ben Armstrong, Kate Larson, Nimrod Talmon
| null | null | 2,022 |
ijcai
|
Time-Constrained Participatory Budgeting Under Uncertain Project Costs
| null |
In participatory budgeting the stakeholders collectively decide which projects from a set of proposed projects should be implemented.
This decision underlies both time and monetary constraints.
In reality it is often impossible to figure out the exact cost of each project in advance, it is only known after a project is finished.
To reduce risk, one can implement projects one after the other to be able to react to higher costs of a previous project.
However, this will increase execution time drastically.
We generalize existing frameworks to capture this setting, study desirable properties of algorithms for this problem, and show that some desirable properties are incompatible.
Then we present and analyze algorithms that trade-off desirable properties.
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Dorothea Baumeister, Linus Boes, Christian Laußmann
| null | null | 2,022 |
ijcai
|
Public Signaling in Bayesian Ad Auctions
| null |
We study signaling in Bayesian ad auctions, in which bidders' valuations depend on a random, unknown state of nature. The auction mechanism has complete knowledge of the actual state of nature, and it can send signals to bidders so as to disclose information about the state and increase revenue. For instance, a state may collectively encode some features of the user that are known to the mechanism only, since the latter has access to data sources unaccessible to the bidders. We study the problem of computing how the mechanism should send signals to bidders in order to maximize revenue. While this problem has already been addressed in the easier setting of second-price auctions, to the best of our knowledge, our work is the first to explore ad auctions with more than one slot. In this paper, we focus on public signaling and VCG mechanisms, under which bidders truthfully report their valuations. We start with a negative result, showing that, in general, the problem does not admit a PTAS unless P = NP, even when bidders' valuations are known to the mechanism. The rest of the paper is devoted to settings in which such negative result can be circumvented. First, we prove that, with known valuations, the problem can indeed be solved in polynomial time when either the number of states d or the number of slots m is fixed. Moreover, in the same setting, we provide an FPTAS for the case in which bidders are single minded, but d and m can be arbitrary. Then, we switch to the random valuations setting, in which these are randomly drawn according to some probability distribution. In this case, we show that the problem admits an FPTAS, a PTAS, and a QPTAS, when, respectively, d is fixed, m is fixed, and bidders' valuations are bounded away from zero.
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Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Giulia Romano, Nicola Gatti
| null | null | 2,022 |
ijcai
|
Fair Equilibria in Sponsored Search Auctions: The Advertisers’ Perspective
| null |
In this work we introduce a new class of mechanisms composed of a traditional Generalized Second Price (GSP) auction, and a fair division scheme in order to achieve some desired level of fairness between groups of Bayesian strategic advertisers. We propose two mechanisms, beta-Fair GSP and GSP-EFX, that compose GSP with, respectively, an envy-free up to one item, and an envy-free up to any item fair division scheme. The payments of GSP are adjusted in order to compensate advertisers that suffer a loss of efficiency due the fair division stage. We investigate the strategic learning implications of the deployment of sponsored search auction mechanisms that obey to such fairness criteria. We prove that, for both mechanisms, if bidders play so as to minimize their external regret they are guaranteed to reach an equilibrium with good social welfare. We also prove that the mechanisms are budget balanced, so that the payments charged by the traditional GSP mechanism are a good proxy of the total compensation offered to the advertisers. Finally, we evaluate the quality of the allocations through experiments on real-world data.
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Georgios Birmpas, Andrea Celli, Riccardo Colini-Baldeschi, Stefano Leonardi
| null | null | 2,022 |
ijcai
|
Distortion in Voting with Top-t Preferences
| null |
A fundamental question in social choice and multi-agent systems is aggregating ordinal preferences expressed by agents into a measurably prudent collective choice. A promising line of recent work views ordinal preferences as a proxy for underlying cardinal preferences. It aims to optimize distortion, the worst-case approximation ratio of the (utilitarian) social welfare. When agents rank the set of alternatives, prior work identifies near-optimal voting rules for selecting one or more alternatives. However, ranking all the alternatives is prohibitive when there are many alternatives.
In this work, we consider the setting where each agent ranks only her t favorite alternatives and identify almost tight bounds on the best possible distortion when selecting a single alternative or a committee of alternatives of a given size k. Our results also extend to approximating higher moments of social welfare. Along the way, we close a gap left open in prior work by identifying asymptotically tight distortion bounds for committee selection given full rankings.
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Allan Borodin, Daniel Halpern, Mohamad Latifian, Nisarg Shah
| null | null | 2,022 |
ijcai
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Let’s Agree to Agree: Targeting Consensus for Incomplete Preferences through Majority Dynamics
| null |
We study settings in which agents with incomplete
preferences need to make a collective decision. We
focus on a process of majority dynamics where issues
are addressed one at a time and undecided
agents follow the opinion of the majority. We assess
the effects of this process on various consensus
notions—such as the Condorcet winner—and show
that in the worst case, myopic adherence to the majority
damages existing consensus; yet, simulation
experiments indicate that the damage is often mild.
We also examine scenarios where the chair of the
decision process can control the existence (or the
identity) of consensus, by determining the order in
which the issues are discussed.
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Sirin Botan, Simon Rey, Zoi Terzopoulou
| null | null | 2,022 |
ijcai
|
Single-Peaked Opinion Updates
| null |
We consider opinion diffusion for undirected networks with sequential updates when the opinions of the agents are single-peaked preference rankings. Our starting point is the study of preserving single-peakedness. We identify voting rules that, when given a single-peaked profile, output at least one ranking that is single peaked w.r.t. a single-peaked axis of the input. For such voting rules we show convergence to a stable state of the diffusion process that uses the voting rule as the agents' update rule. Further, we establish an efficient algorithm that maximises the spread of extreme opinions.
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Robert Bredereck, Anne-Marie George, Jonas Israel, Leon Kellerhals
| null | null | 2,022 |
ijcai
|
Toward Policy Explanations for Multi-Agent Reinforcement Learning
| null |
Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving. Explaining agent decisions is crucial for improving system transparency, increasing user satisfaction, and facilitating human-agent collaboration. However, existing works on explainable reinforcement learning mostly focus on the single-agent setting and are not suitable for addressing challenges posed by multi-agent environments. We present novel methods to generate two types of policy explanations for MARL: (i) policy summarization about the agent cooperation and task sequence, and (ii) language explanations to answer queries about agent behavior. Experimental results on three MARL domains demonstrate the scalability of our methods. A user study shows that the generated explanations significantly improve user performance and increase subjective ratings on metrics such as user satisfaction.
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Kayla Boggess, Sarit Kraus, Lu Feng
| null | null | 2,022 |
ijcai
|
When Votes Change and Committees Should (Not)
| null |
Electing a single committee of a small size is a classical and well-understood voting situation. Being interested in a sequence of committees, we introduce two time-dependent multistage models based on simple scoring-based voting. Therein, we are given a sequence of voting profiles (stages) over the same set of agents and candidates, and our task is to find a small committee for each stage of high score. In the conservative model we additionally require that any two consecutive committees have a small symmetric difference. Analogously, in the revolutionary model we require large symmetric differences. We prove both models to be NP-hard even for a constant number of agents, and, based on this, initiate a parameterized complexity analysis for the most natural parameters and combinations thereof. Among other results, we prove both models to be in XP yet W[1]-hard regarding the number of stages, and that being revolutionary seems to be "easier" than being conservative.
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Robert Bredereck, Till Fluschnik, Andrzej Kaczmarczyk
| null | null | 2,022 |
ijcai
|
Two-Sided Matching over Social Networks
| null |
A new paradigm of mechanism design, called mechanism design over social networks, investigates agents’ incentives to diffuse the information of mechanisms to their followers over social networks. In this paper we consider it for two-sided matching, where the agents on one side, say students, are distributed over social networks and thus are not fully observable to the mechanism designer, while the agents on the other side, say colleges, are known a priori. The main purpose of this paper is to clarify the existence of mechanisms that satisfy several properties that are classified into four criteria: incentive constraints, efficiency constraints, stability constraints, and fairness constraints. We proposed three mechanisms and showed that no mechanism is better than these mechanisms, i.e., they are in the Pareto frontier according to the set of properties defined in this paper.
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Sung-Ho Cho, Taiki Todo, Makoto Yokoo
| null | null | 2,022 |
ijcai
|
Tolerance is Necessary for Stability: Single-Peaked Swap Schelling Games
| null |
Residential segregation in metropolitan areas is a phenomenon that can be observed all over the world. Recently, this was investigated via game-theoretic models. There, selfish agents of two types are equipped with a monotone utility function that ensures higher utility if an agent has more same-type neighbors. The agents strategically choose their location on a given graph that serves as residential area to maximize their utility. However, sociological polls suggest that real-world agents are actually favoring mixed-type neighborhoods, and hence should be modeled via non-monotone utility functions. To address this, we study Swap Schelling Games with single-peaked utility functions. Our main finding is that tolerance, i.e., agents favoring fifty-fifty neighborhoods or being in the minority, is necessary for equilibrium existence on almost regular or bipartite graphs. Regarding the quality of equilibria, we derive (almost) tight bounds on the Price of Anarchy and the Price of Stability. In particular, we show that the latter is constant on bipartite and almost regular graphs.
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Davide Bilò, Vittorio Bilò, Pascal Lenzner, Louise Molitor
| null | null | 2,022 |
ijcai
|
General Opinion Formation Games with Social Group Membership
| null |
Modeling how agents form their opinions is of paramount importance for designing marketing and electoral campaigns. In this work, we present a new framework for opinion formation which generalizes the well-known Friedkin-Johnsen model by incorporating three important features: (i) social group membership, that limits the amount of influence that people not belonging to the same group may lead on a given agent; (ii) both attraction among friends, and repulsion among enemies; (iii) different strengths of influence lead from different people on a given agent, even if the social relationships among them are the same.
We show that, despite its generality, our model always admits a pure Nash equilibrium which, under opportune mild conditions, is even unique. Next, we analyze the performances of these equilibria with respect to a social objective function defined as a convex combination, parametrized by a value λ∈[0,1], of the costs yielded by the untruthfulness of the declared opinions and the total cost of social pressure. We prove bounds on both the price of anarchy and the price of stability which show that, for not-too-extreme values of λ, performance at equilibrium are very close to optimal ones. For instance, in several interesting scenarios, the prices of anarchy and stability are both equal to max{2λ,1-λ}/min{2λ,1-λ} which never exceeds 2 for λ∈[1/5,1/2].
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Vittorio Bilò, Diodato Ferraioli, Cosimo Vinci
| null | null | 2,022 |
ijcai
|
Goal Consistency: An Effective Multi-Agent Cooperative Method for Multistage Tasks
| null |
Although multistage tasks involving multiple sequential goals are common in real-world applications, they are not fully studied in multi-agent reinforcement learning (MARL). To accomplish a multi-stage task, agents have to achieve cooperation on different subtasks. Exploring the collaborative patterns of different subtasks and the sequence of completing the subtasks leads to an explosion in the search space, which poses great challenges to policy learning. Existing works designed for single-stage tasks where agents learn to cooperate only once usually suffer from low sample efficiency in multi-stage tasks as agents explore aimlessly. Inspired by human’s improving cooperation through goal consistency, we propose Multi-Agent Goal Consistency (MAGIC) framework to improve sample efficiency for learning in multi-stage tasks. MAGIC adopts a goal-oriented actor-critic model to learn both local and global views of goal cognition, which helps agents understand the task at the goal level so that they can conduct targeted exploration accordingly. Moreover, to improve exploration efficiency, MAGIC employs two-level goal consistency training to drive agents to formulate a consistent goal cognition. Experimental results show that MAGIC significantly improves sample efficiency and facilitates cooperation among agents compared with state-of-art MARL algorithms in several challenging multistage tasks.
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Xinning Chen, Xuan Liu, Shigeng Zhang, Bo Ding, Kenli Li
| null | null | 2,022 |
ijcai
|
Incentives in Social Decision Schemes with Pairwise Comparison Preferences
| null |
Social decision schemes (SDSs) map the preferences of individual voters over multiple alternatives to a probability distribution over the alternatives. In order to study properties such as efficiency, strategyproofness, and participation for SDSs, preferences over alternatives are typically lifted to preferences over lotteries using the notion of stochastic dominance (SD). However, requiring strategyproofness or strict participation with respect to this preference extension only leaves room for rather undesirable SDSs such as random dictatorships. Hence, we focus on the natural but little understood pairwise comparison (PC) preference extension, which postulates that one lottery is preferred to another if the former is more likely to return a preferred outcome. In particular, we settle three open questions raised by Brandt in Rolling the dice: Recent results in probabilistic social choice (2017): (i) there is no Condorcet-consistent SDS that satisfies PC-strategyproofness; (ii) there is no anonymous and neutral SDS that satisfies PC-efficiency and PC-strategyproofness; and (iii) there is no anonymous and neutral SDS that satisfies PC-efficiency and strict PC-participation. All three impossibilities require m>=4 alternatives and turn into possibilities when m<=3.
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Felix Brandt, Patrick Lederer, Warut Suksompong
| null | null | 2,022 |
ijcai
|
VidyutVanika21: An Autonomous Intelligent Broker for Smart-grids
| null |
An autonomous broker that liaises between retail customers and power-generating companies (GenCos) is essential for the smart grid ecosystem. The efficiency brought in by such brokers to the smart grid setup can be studied through a well-developed simulation environment. In this paper, we describe the design of one such energy broker called VidyutVanika21 (VV21) and analyze its performance using a simulation platform called PowerTAC (PowerTrading Agent Competition). Specifically, we discuss the retail (VV21–RM) and wholesale market (VV21–WM) modules of VV21 that help the broker achieve high net profits in a competitive setup. Supported by game-theoretic analysis, the VV21–RM designs tariff contracts that a) maintain a balanced portfolio of different types of customers; b) sustain an appropriate level of market share, and c) introduce surcharges on customers to reduce energy usage during peak demand times. The VV21–WM aims to reduce the cost of procurement by following the supply curve of the GenCo to identify its lowest ask for a particular auction which is then used to generate suitable bids. We further demonstrate the efficacy of the retail and wholesale strategies of VV21 in PowerTAC 2021 finals and through several controlled experiments.
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Sanjay Chandlekar, Bala Suraj Pedasingu, Easwar Subramanian, Sanjay Bhat, Praveen Paruchuri, Sujit Gujar
| null | null | 2,022 |
ijcai
|
Understanding Distance Measures Among Elections
| null |
Motivated by putting empirical work based on (synthetic) election data on a more solid mathematical basis, we analyze six distances among elections, including, e.g., the challenging-to-compute but very precise swap distance and the distance used to form the so-called map of elections. Among the six, the latter seems to strike the best balance between its computational complexity and expressiveness.
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Niclas Boehmer, Piotr Faliszewski, Rolf Niedermeier, Stanisław Szufa, Tomasz Wąs
| null | null | 2,022 |
ijcai
|
A Formal Model for Multiagent Q-Learning Dynamics on Regular Graphs
| null |
Modeling the dynamics of multi-agent learning has long been an important research topic. The focus of previous research has been either on 2-agent settings or well-mixed infinitely large agent populations. In this paper, we consider the scenario where n Q-learning agents locate on regular graphs, such that agents can only interact with their neighbors. We examine the local interactions between individuals and their neighbors, and derive a formal model to capture the Q-value dynamics of the entire population. Through comparisons with agent-based simulations on different types of regular graphs, we show that our model describes the agent learning dynamics in an exact manner.
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Chen Chu, Yong Li, Jinzhuo Liu, Shuyue Hu, Xuelong Li, Zhen Wang
| null | null | 2,022 |
ijcai
|
Network Creation with Homophilic Agents
| null |
Network Creation Games are an important framework for understanding the formation of real-world networks. These games usually assume a set of indistinguishable agents strategically buying edges at a uniform price leading to a network among them. However, in real life, agents are heterogeneous and their relationships often display a bias towards similar agents, say of the same ethnic group. This homophilic behavior on the agent level can then lead to the emergent global phenomenon of social segregation. We study Network Creation Games with multiple types of homophilic agents and non-uniform edge cost, introducing two models focusing on the perception of same-type and different-type neighboring agents, respectively. Despite their different initial conditions, both our theoretical and experimental analysis show that both the composition and segregation strength of the resulting stable networks are almost identical, indicating a robust structure of social networks under homophily.
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Martin Bullinger, Pascal Lenzner, Anna Melnichenko
| null | null | 2,022 |
ijcai
|
Multi-Agent Intention Progression with Reward Machines
| null |
Recent work in multi-agent intention scheduling has shown that enabling agents to predict the actions of other agents when choosing their own actions can be beneficial. However existing approaches to 'intention-aware' scheduling assume that the programs of other agents are known, or are "similar" to that of the agent making the prediction. While this assumption is reasonable in some circumstances, it is less plausible when the agents are not co-designed. In this paper, we present a new approach to multi-agent intention scheduling in which agents predict the actions of other agents based on a high-level specification of the tasks performed by an agent in the form of a reward machine (RM) rather than on its (assumed) program. We show how a reward machine can be used to generate tree and rollout policies for an MCTS-based scheduler. We evaluate our approach in a range of multi-agent environments, and show that RM-based scheduling out-performs previous intention-aware scheduling approaches in settings where agents are not co-designed
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Michael Dann, Yuan Yao, Natasha Alechina, Brian Logan, John Thangarajah
| null | null | 2,022 |
ijcai
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Approval with Runoff
| null |
We define a family of runoff rules that work as follows: voters cast approval ballots over candidates; two finalists are selected; and the winner is decided by majority. With approval-type ballots, there are various ways to select the finalists. We leverage known approval-based committee rules and study the obtained runoff rules from an axiomatic point of view. Then we analyze the outcome of these rules on single-peaked profiles, and on real data.
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Théo Delemazure, Jérôme Lang, Jean-François Laslier, M. Remzi Sanver
| null | null | 2,022 |
ijcai
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Online Approval Committee Elections
| null |
Assume k candidates need to be selected. The candidates appear over time. Each time one appears, it must be immediately selected or rejected---a decision that is made by a group of individuals through voting. Assume the voters use approval ballots, i.e., for each candidate they only specify whether they consider it acceptable or not. This setting can be seen as a voting variant of choosing k secretaries. Our contribution is twofold. (1) We assess to what extent the committees that are computed online can proportionally represent the voters. (2) If a prior probability over candidate approvals is available, we show how to compute committees with maximal expected score.
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Virginie Do, Matthieu Hervouin, Jérôme Lang, Piotr Skowron
| null | null | 2,022 |
ijcai
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Preserving Consistency in Multi-Issue Liquid Democracy
| null |
Liquid democracy bridges the gap between direct and representative democracy by allowing agents to vote directly on an issue or delegate to a trusted voter. Yet, when applied to votes on multiple interconnected issues, liquid democracy can lead agents to submit inconsistent votes. Two approaches are possible to maintain consistency: either modify the voters' ballots by ignoring problematic delegations, or resolve all delegations and make changes to the final votes of the agents. We show that rules based on minimising such changes are NP-complete. We propose instead to elicit and apply the agents' priorities over the delegated issues, designing and analysing two algorithms that find consistent votes from the agents' delegations in polynomial time.
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Rachael Colley, Umberto Grandi
| null | null | 2,022 |
ijcai
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Voting in Two-Crossing Elections
| null |
We introduce two-crossing elections as a generalization of single-crossing elections, showing a number of new results. First, we show that two-crossing elections can be recognized in polynomial time, by reduction to the well-studied consecutive ones problem. Single-crossing elections exhibit a transitive majority relation, from which many important results follow. On the other hand, we show that the classical Debord-McGarvey theorem can still be proven two-crossing, implying that any weighted majority tournament is inducible by a two-crossing election. This shows that many voting rules are NP-hard under two-crossing elections, including Kemeny and Slater. This is in contrast to the single-crossing case and outlines an important complexity boundary between single- and two-crossing. Subsequently, we show that for two-crossing elections the Young scores of all candidates can be computed in polynomial time, by formulating a totally unimodular linear program. Finally, we consider the Chamberlin-Courant rule with arbitrary disutilities and show that a winning committee can be computed in polynomial time, using an approach based on dynamic programming.
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Andrei Constantinescu, Roger Wattenhofer
| null | null | 2,022 |
ijcai
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An Analysis of the Linear Bilateral ANAC Domains Using the MiCRO Benchmark Strategy
| null |
The Automated Negotiating Agents Competition (ANAC) is an annual competition that compares the state-of-the-art algorithms in the field of automated negotiation. Although in recent years ANAC has given more and more attention to more complex scenarios, the linear and bilateral negotiation domains that were used for its first few editions are still widely used as the default benchmark in automated negotiations research. In this paper, however, we argue that these domains should no longer be used, because they are too simplistic. We demonstrate this with an extremely simple new negotiation strategy called MiCRO, which does not employ any form of opponent modeling or machine learning, but nevertheless outperforms the strongest participants of ANAC 2012, 2013, 2018 and 2019. Furthermore, we provide a theoretical analysis which explains why MiCRO performs so well in the ANAC domains. This analysis may help researchers to design more challenging negotiation domains in the future.
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Dave De Jonge
| null | null | 2,022 |
ijcai
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The Complexity of Envy-Free Graph Cutting
| null |
We consider the problem of fairly dividing a set of heterogeneous divisible resources among agents with different preferences. We focus on the setting where the resources correspond to the edges of a connected graph, every agent must be assigned a connected piece of this graph, and the fairness notion considered is the classical envy freeness. The problem is NP-complete, and we analyze its complexity with respect to two natural complexity measures: the number of agents and the number of edges in the graph. While the problem remains NP-hard even for instances with 2 agents, we provide a dichotomy characterizing the complexity of the problem when the number of agents is constant based on structural properties of the graph. For the latter case, we design a polynomial-time algorithm when the graph has a constant number of edges.
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Argyrios Deligkas, Eduard Eiben, Robert Ganian, Thekla Hamm, Sebastian Ordyniak
| null | null | 2,022 |
ijcai
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Optimal Anonymous Independent Reward Scheme Design
| null |
We consider designing reward schemes that incentivize agents to create high-quality content (e.g., videos, images, text, ideas). The problem is at the center of a real-world application where the goal is to optimize the overall quality of generated content on user-generated content platforms. We focus on anonymous independent reward schemes (AIRS) that only take the quality of an agent's content as input. We prove the general problem is NP-hard. If the cost function is convex, we show the optimal AIRS can be formulated as a convex optimization problem and propose an efficient algorithm to solve it. Next, we explore the optimal linear reward scheme and prove it has a 1/2-approximation ratio, and the ratio is tight. Lastly, we show the proportional scheme can be arbitrarily bad compared to AIRS.
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Mengjing Chen, Pingzhong Tang, Zihe Wang, Shenke Xiao, Xiwang Yang
| null | null | 2,022 |
ijcai
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Insight into Voting Problem Complexity Using Randomized Classes
| null |
The first step in classifying the complexity of an NP problem is typically showing the problem in P or NP-complete. This has been a successful first step for many problems, including voting problems. However, in this paper we show that this may not always be the best first step. We consider the problem of constructive control by replacing voters (CCRV) introduced by Loreggia et al. [2015, https://dl.acm.org/doi/10.5555/2772879.2773411] for the scoring rule First-Last, which is defined by (1, 0, ..., 0, -1). We show that this problem is equivalent to Exact Perfect Bipartite Matching, and so CCRV for First-Last can be determined in random polynomial time. So on the one hand, if CCRV for First-Last is NP-complete then RP = NP, which is extremely unlikely. On the other hand, showing that CCRV for First-Last is in P would also show that Exact Perfect Bipartite Matching is in P, which would solve a well-studied 40-year-old open problem.
Considering RP as an option for classifying problems can also help classify problems that until now had escaped classification. For example, the sole open problem in the comprehensive table from Erdélyi et al. [2021, https://doi.org/10.1007/s10458-021-09523-9] is CCRV for 2-Approval. We show that this problem is in RP, and thus easy since it is widely assumed that P = RP.
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Zack Fitzsimmons, Edith Hemaspaandra
| null | null | 2,022 |
ijcai
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Parameterized Complexity of Hotelling-Downs with Party Nominees
| null |
We study a generalization of the Hotelling-Downs model through the lens of parameterized complexity. In this model, there is a set of voters on a line and a set of parties that compete over them. Each party has to choose a nominee from a set of candidates with predetermined positions on the line, where each candidate comes at a different cost. The goal of every party is to choose the most profitable nominee, given the nominees chosen by the rest of the parties; the profit of a party is the number of voters closer to their nominee minus its cost. We examine the complexity of deciding whether a pure Nash equilibrium exists for this model under several natural parameters: the number of different positions of the candidates, the discrepancy and the span of the nominees, and the overlap of the parties. We provide FPT and XP algorithms and we complement them with a W[1]-hardness result.
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Argyrios Deligkas, Eduard Eiben, Tiger-Lily Goldsmith
| null | null | 2,022 |
ijcai
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On the Ordinal Invariance of Power Indices on Coalitional Games
| null |
In a coalitional game, the coalitions are weakly ordered according to their worths in the game. When moreover a power index is given, the players are ranked according to the real numbers they are assigned by the power index. If any game inducing the same ordering of the coalitions generates the same ranking of the players then, by definition, the game is (ordinally) stable for the power index, which in turn is ordinally invariant for the game. If one is interested in ranking players of a game which is stable, re-computing the power indices when the coalitional worths slightly fluctuate or are uncertain becomes useless. Bivalued games are easy examples of games stable for any power index which is linear. Among general games, we characterize those that are stable for a given linear index. Note that the Shapley and Banzhaf scores, frequently used in AI, are particular semivalues, and all semivalues are linear indices. To check whether a game is stable for a specific semivalue, it suffices to inspect the ordering of the coalitions and to perform some direct computation based on the semivalue parameters.
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Jean-Paul Doignon, Stefano Moretti, Meltem Ozturk
| null | null | 2,022 |
ijcai
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Invasion Dynamics in the Biased Voter Process
| null |
The voter process is a classic stochastic process that models the invasion of a mutant trait A (e.g., a new opinion, belief, legend, genetic mutation, magnetic spin) in a population of agents (e.g., people, genes, particles) who share a resident trait B, spread over the nodes of a graph. An agent may adopt the trait of one of its neighbors at any time, while the invasion bias r quantifies the stochastic preference towards (r>1) or against (r<1) adopting A over B. Success is measured in terms of the fixation probability, i.e., the probability that eventually all agents have adopted the mutant trait A. In this paper we study the problem of fixation probability maximization under this model: given a budget k, find a set of k agents to initiate the invasion that maximizes the fixation probability. We show that the problem is NP-hard for both regimes r>1 and r<1, while the latter case is also inapproximable within any multiplicative factor that is independent of r. On the positive side, we show that when r>1, the optimization function is submodular and thus can be greedily approximated within a factor 1-1/e. An experimental evaluation of some proposed heuristics corroborates our results.
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Loke Durocher, Panagiotis Karras, Andreas Pavlogiannis, Josef Tkadlec
| null | null | 2,022 |
ijcai
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On the Convergence of Fictitious Play: A Decomposition Approach
| null |
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in n-player games, which builds the foundation for modern multi-agent learning algorithms. Although FP has provable convergence guarantees on zero-sum games and potential games, many real-world problems are often a mixture of both and the convergence property of FP has not been fully studied yet. In this paper, we extend the convergence results of FP to the combinations of such games and beyond. Specifically, we derive new conditions for FP to converge by leveraging game decomposition techniques. We further develop a linear relationship unifying cooperation and competition in the sense that these two classes of games are mutually transferable. Finally, we analyse a non-convergent example of FP, the Shapley game, and develop sufficient conditions for FP to converge.
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Yurong Chen, Xiaotie Deng, Chenchen Li, David Mguni, Jun Wang, Xiang Yan, Yaodong Yang
| null | null | 2,022 |
ijcai
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Approximate Strategyproof Mechanisms for the Additively Separable Group Activity Selection Problem
| null |
We investigate strategyproof mechanisms in the Group Activity Selection Problem with the additively separable property. Namely, agents have distinct preferences for each activity and individual weights for the other agents. We evaluate our mechanisms in terms of their approximation ratio with respect to the maximum utilitarian social welfare.
We first show that, for arbitrary non-negative preferences, no deterministic mechanism can achieve a bounded approximation ratio. Thus, we provide a randomized k-approximate mechanism, where k is the number of activities, and a corresponding 2-2/(k+1) lower bound. Furthermore, we propose a tight (2 - 1/k)-approximate randomized mechanism when activities are copyable.
We then turn our attention to instances where preferences can only be unitary, that is 0 or 1. In this case, we provide a k-approximate deterministic mechanism, which we show to be the best possible one within the class of strategyproof and anonymous mechanisms. We also provide a general lower bound of Ω({\sqrt{k}) when anonymity is no longer a constraint. Finally, we focus on unitary preferences and weights, and prove that, while any mechanism returning the optimum is not strategyproof, there exists a 2-approximate deterministic mechanism.
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Michele Flammini, Giovanna Varricchio
| null | null | 2,022 |
ijcai
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Representation Matters: Characterisation and Impossibility Results for Interval Aggregation
| null |
In the context of aggregating intervals reflecting the views of several agents into a single interval, we investigate the impact of the form of representation chosen for the intervals involved. Specifically, we ask whether there are natural rules we can define both as rules that aggregate separately the left and right endpoints of intervals and as rules that aggregate separately the left endpoints and the interval widths. We show that on discrete scales it is essentially impossible to do so, while on continuous scales we can characterise the rules meeting these requirements as those that compute a weighted average of the endpoints of the individual intervals.
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Ulle Endriss, Arianna Novaro, Zoi Terzopoulou
| null | null | 2,022 |
ijcai
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Can Buyers Reveal for a Better Deal?
| null |
We study market interactions in which buyers are allowed to credibly reveal partial information about their types to the seller. Previous recent work has studied the special case of one buyer and one good, showing that such communication can simultaneously improve social welfare and ex ante buyer utility. However, with multiple buyers, we find that the buyer-optimal signalling schemes from the one-buyer case are actually harmful to buyer welfare. Moreover, we prove several impossibility results showing that, with either multiple i.i.d. buyers or multiple i.i.d. goods, maximizing buyer utility can be at odds with social efficiency, which is surprising in contrast with the one-buyer, one-good case. Finally, we investigate the computational tractability of implementing desirable equilibrium outcomes. We find that, even with one buyer and one good, optimizing buyer utility is generally NP-hard but tractable in a practical restricted setting.
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Daniel Halpern, Gregory Kehne, Jamie Tucker-Foltz
| null | null | 2,022 |
ijcai
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Picking the Right Winner: Why Tie-Breaking in Crowdsourcing Contests Matters
| null |
We present a complete information game-theoretic model for crowdsourcing contests. We observe that in design contests, coding contests and other domains, separating low quality submissions from high quality ones is often easy. However, pinning down the best submission is more challenging since there is no objective measure. We model this situation by assuming that each contestant has an ability, which we interpret as its probability of submitting a high-quality submission. After the contestants decide whether or not they want to participate, the organizer of the contest needs to break ties between the high quality submissions. A common assumption in the literature is that the exact tie-breaking rule does not matter as long as ties are broken consistently. However, we show that the choice of the tie-breaking rule may have significant implications on the efficiency of the contest.
Our results highlight both qualitative and quantitative differences between various deterministic tie-breaking rules. Perhaps counterintuitively, we show that in many scenarios, the utility of the organizer is maximized when ties are broken in favor of successful players with lower ability. Nevertheless, we show that the natural rule of choosing the submission of the successful player with the highest ability guarantees the organizer at least 1/3 of its utility under any tie-breaking rule. To complement these results, we provide an upper bound of Hn ~ \ln(n) on the price of anarchy (the ratio between the social welfare of the optimal solution and the social welfare of the Nash equilibrium). We show that this ratio is tight when ties are broken in favor of players with higher abilities.
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Coral Haggiag, Sigal Oren, Ella Segev
| null | null | 2,022 |
ijcai
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Two for One & One for All: Two-Sided Manipulation in Matching Markets
| null |
Strategic behavior in two-sided matching markets has been traditionally studied in a "one-sided" manipulation setting where the agent who misreports is also the intended beneficiary. Our work investigates "two-sided" manipulation of the deferred acceptance algorithm where the misreporting agent and the manipulator (or beneficiary) are on different sides. Specifically, we generalize the recently proposed accomplice manipulation model (where a man misreports on behalf of a woman) along two complementary dimensions: (a) the two for one model, with a pair of misreporting agents (man and woman) and a single beneficiary (the misreporting woman), and (b) the one for all model, with one misreporting agent (man) and a coalition of beneficiaries (all women). Our main contribution is to develop polynomial-time algorithms for finding an optimal manipulation in both settings. We obtain these results despite the fact that an optimal one for all strategy fails to be inconspicuous, while it is unclear whether an optimal two for one strategy satisfies the inconspicuousness property. We also study the conditions under which stability of the resulting matching is preserved. Experimentally, we show that two-sided manipulations are more frequently available and offer better quality matches than their one-sided counterparts.
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Hadi Hosseini, Fatima Umar, Rohit Vaish
| null | null | 2,022 |
ijcai
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Efficient Resource Allocation with Secretive Agents
| null |
We consider the allocation of homogeneous divisible goods to agents with linear additive valuations. Our focus is on the case where some agents are secretive and reveal no preference information, while the remaining agents reveal full preference information. We study distortion, which is the worst-case approximation ratio when maximizing social welfare given such partial information about agent preferences. As a function of the number of secretive agents k relative to the overall number of agents n, we identify the exact distortion for every p-mean welfare function, which includes the utilitarian welfare (p=1), the Nash welfare (p -> 0), and the egalitarian welfare (p -> -Inf).
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Soroush Ebadian, Rupert Freeman, Nisarg Shah
| null | null | 2,022 |
ijcai
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Contests to Incentivize a Target Group
| null |
We study how to incentivize agents in a target subpopulation to produce a higher output by means of rank-order allocation contests, in the context of incomplete information. We describe a symmetric Bayes--Nash equilibrium for contests that have two types of rank-based prizes: (1) prizes that are accessible only to the agents in the target group; (2) prizes that are accessible to everyone. We also specialize this equilibrium characterization to two important sub-cases: (i) contests that do not discriminate while awarding the prizes, i.e., only have prizes that are accessible to everyone; (ii) contests that have prize quotas for the groups, and each group can compete only for prizes in their share. For these models, we also study the properties of the contest that maximizes the expected total output by the agents in the target group.
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Edith Elkind, Abheek Ghosh, Paul W. Goldberg
| null | null | 2,022 |
ijcai
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Forgiving Debt in Financial Network Games
| null |
We consider financial networks, where nodes correspond to banks and directed labeled edges correspond to debt contracts between banks. Maximizing systemic liquidity, i.e., the total money flow, is a natural objective of any financial authority. In particular, the financial authority may offer bailout money to some bank(s) or forgive the debts of others in order to maximize liquidity, and we examine efficient ways to achieve this. We study the computational hardness of finding the optimal debt-removal and budget-constrained optimal bailout policy, respectively, and we investigate the approximation ratio provided by the greedy bailout policy compared to the optimal one.
We also study financial systems from a game-theoretic standpoint. We observe that the removal of some incoming debt might be in the best interest of a bank. Assuming that a bank's well-being (i.e., utility) is aligned with the incoming payments they receive from the network, we define and analyze a game among banks who want to maximize their utility by strategically giving up some incoming payments. In addition, we extend the previous game by considering bailout payments. After formally defining the above games, we prove results about the existence and quality of pure Nash equilibria, as well as the computational complexity of finding such equilibria.
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Panagiotis Kanellopoulos, Maria Kyropoulou, Hao Zhou
| null | null | 2,022 |
ijcai
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Plurality Veto: A Simple Voting Rule Achieving Optimal Metric Distortion
| null |
The metric distortion framework posits that n voters and m candidates are jointly embedded in a metric space such that voters rank candidates that are closer to them higher.
A voting rule's purpose is to pick a candidate with minimum total distance to the voters, given only the rankings, but not the actual distances.
As a result, in the worst case, each deterministic rule picks a candidate whose total distance is at least three times larger than that of an optimal one, i.e., has distortion at least 3.
A recent breakthrough result showed that achieving this bound of 3 is possible;
however, the proof is non-constructive, and the voting rule itself is a complicated exhaustive search.
Our main result is an extremely simple voting rule, called Plurality Veto, which achieves the same optimal distortion of 3.
Each candidate starts with a score equal to his number of first-place votes.
These scores are then gradually decreased via an n-round veto process in which a candidate drops out when his score reaches zero. One after the other, voters decrement the score of their bottom choice among the standing candidates, and the last standing candidate wins.
We give a one-paragraph proof that this voting rule achieves distortion 3.
This rule is also immensely practical, and it only makes two queries to each voter, so it has low communication overhead.
We also show that a straightforward extension can be used to give a constructive proof of the more general Ranking-Matching Lemma of Gkatzelis et al.
We also generalize Plurality Veto into a class of randomized voting rules in the following way: Plurality veto is run only for k < n rounds; then, a candidate is chosen with probability proportional to his residual score.
This general rule interpolates between Random Dictatorship (for k=0) and Plurality Veto (for k=n-1), and k controls the variance of the output.
We show that for all k, this rule has expected distortion at most 3.
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Fatih Erdem Kizilkaya, David Kempe
| null | null | 2,022 |
ijcai
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Propositional Gossip Protocols under Fair Schedulers
| null |
Gossip protocols are programs that can be used by a group of agents to synchronize what information they have. Namely, assuming each agent holds a secret, the goal of a protocol is to reach a situation in which all agents know all secrets. Distributed epistemic gossip protocols use epistemic formulas in the component programs for the agents. In this paper, we study the simplest classes of such gossip protocols: propositional gossip protocols, in which whether an agent wants to initiate a call depends only on the set of secrets that the agent currently knows. It was recently shown that such a protocol can be correct, i.e., always terminates in a state where all agents know all secrets, only when its communication graph is complete. We show here that this characterization dramatically changes when the usual fairness constraints are imposed on the call scheduler used. Finally, we establish that checking the correctness of a given propositional protocol under a fair scheduler is a coNP-complete problem.
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Joseph Livesey, Dominik Wojtczak
| null | null | 2,022 |
ijcai
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The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations
| null |
While the stable marriage problem and its variants model a vast range of matching markets, they fail to capture complex agent relationships, such as the affiliation of applicants and employers in an interview marketplace. To model this problem, the existing literature on matching with externalities permits agents to provide complete and total rankings over matchings based off of both their own and their affiliates' matches. This complete ordering restriction is unrealistic, and further the model may have an empty core. To address this, we introduce the Dichotomous Affiliate Stable Matching (DASM) Problem, where agents' preferences indicate dichotomous acceptance or rejection of another agent in the marketplace, both for themselves and their affiliates. We also assume the agent's preferences over entire matchings are determined by a general weighted valuation function of their (and their affiliates') matches. Our results are threefold: (1) we use a human study to show that real-world matching rankings follow our assumed valuation function; (2) we prove that there always exists a stable solution by providing an efficient, easily-implementable algorithm that finds such a solution; and (3) we experimentally validate the efficiency of our algorithm versus a linear-programming-based approach.
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Marina Knittel, Samuel Dooley, John Dickerson
| null | null | 2,022 |
ijcai
|
Biased Majority Opinion Dynamics: Exploiting Graph k-domination
| null |
We study opinion dynamics in multi-agent networks where agents hold binary opinions and are influenced by their neighbors while being biased towards one of the two opinions, called the superior opinion. The dynamics is modeled by the following process: at each round, a randomly selected agent chooses the superior opinion with some probability α, and with probability 1-α it conforms to the opinion manifested by the majority of its neighbors. In this work, we exhibit classes of network topologies for which we prove that the expected time for consensus on the superior opinion can be exponential. This answers an open conjecture in the literature. In contrast, we show that in all cubic graphs, convergence occurs after a polynomial number of rounds for every α.
We rely on new structural graph properties by characterizing the opinion formation in terms of multiple domination, stable and decreasing structures in graphs, providing an interplay between bias, consensus and network structure. Finally, we provide both theoretical and experimental evidence for the existence of decreasing structures and relate it to the rich behavior observed on the expected convergence time of the opinion diffusion model.
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Hicham Lesfari, Frédéric Giroire, Stéphane Pérennes
| null | null | 2,022 |
ijcai
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Explaining Preferences by Multiple Patterns in Voters’ Behavior
| null |
In some preference aggregation scenarios, voters' preferences are highly structured: e.g., the set of candidates may have one-dimensional structure (so that voters' preferences are single-peaked) or be described by a binary decision tree (so that voters' preferences are group-separable). However, sometimes a single axis or a decision tree is insufficient to capture the voters' preferences; rather, there is a small number K of axes or decision trees such that each vote in the profile is consistent with one of these axes (resp., trees). In this work, we study the complexity of deciding whether voters' preferences can be explained in this manner. For K=2, we use the technique developed by Yang [2020, https://doi.org/10.3233/FAIA200099] in the context of single-peaked preferences to obtain a polynomial-time algorithm for several domains: value-restricted preferences, group-separable preferences, and a natural subdomain of group-separable preferences, namely, caterpillar group-separable preferences. For K > 2, the problem is known to be hard for single-peaked preferences; we establish that it is also hard for value-restricted and group-separable preferences. Our positive results for K=2 make use of forbidden minor characterizations of the respective domains; in particular, we establish that the domain of caterpillar group-separable preferences admits a forbidden minor characterization.
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Sonja Kraiczy, Edith Elkind
| null | null | 2,022 |
ijcai
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Phragmén Rules for Degressive and Regressive Proportionality
| null |
We study two concepts of proportionality in the model of approval-based committee elections. In degressive proportionality small minorities of voters are favored in comparison with the standard linear proportionality. Regressive proportionality, on the other hand, requires that larger subdivisions of voters are privileged. We introduce a new family of rules that broadly generalize Phragmén's Sequential Rule spanning the spectrum between degressive and regressive proportionality. We analyze and compare the two principles of proportionality assuming the voters and the candidates can be represented as points in an Euclidean issue space.
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Michał Jaworski, Piotr Skowron
| null | null | 2,022 |
ijcai
|
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