Structuring Knowledge In Vague Domains.
AbstractIn this paper, we propose a model for structuring knowledge in vague and continuous domains where similarity plays a role in coming up with plausible inferences. The model consists of two levels, one of which is an inference network with nodes representing concepts and links representing rules connecting concepts, and the other is a microfeature-based replica of the first level. Based on the interaction between the concept nodes and microfeature nodes in the model, inferences are facilitated and knowledge not explicitly encoded in a system can be deduced via mixed similarity matching and rule application. The model is able to take account of many important desiderata of plausible reasoning and produces sensible conclusions accordingly. Examples will be presented to illustrate the utility of the model in structuring knowledge to enable useful inferences to be carried out in several domains.

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A Connectionist Scheme For Modeling Context
Symbolic Logic and echanical Theorem Proving
The Logic of Plausible Reasoning: A Core Theory
Mundane reasoning by parallel constraint satisfaction
An Introduction to Possibilistic and Fuzzy Logics
Neural Representation of Conceptual Knowledge
Recognition of Semantically Incorrect Rules
Connectionist Expert Systems
Integrating Knowledge-based Systems and Neural Networks for Robotic Skill Acquisition
The Adaptive Brain
In defence of logic
Marker Passing and Microfeature

Multilayer feedforward networks are universal
Backpropagation learning in expert networks
Frame selection in a connectionist model
Principle of Reasoning

The Society of ind
Probabilistic Reasoning in Intelligent Systems


the PDP Research Group
A athematical Theory of Evidence
Designing inference engines based on a discrete neural network model
Rules and Connectionism
The Discrete Neuronal Models
The Discrete Neuronal Models and the Discrete Neuronal Models
Chunking and Connectionism
Neurally Inspired Massively Parallel Model of Rule-Based Reasoning
A connectionist model of commonsense reasoning incorporating rules and similarities.
Beyond associative memories
The athematics of Inheritance
Refinement of approximate domain theories by knowledge-based neural networks
Features of Similarity
Similarity and Analogical Reasoning
Massively Parallel Parsing
Fuzzy Sets

Fuzzy Logic
An Introduction to Expert Systems
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knowledge-based systems;vagueness;artificial intelligence;reasoning;knowledge representation;neural networks