We present ASP Modulo Space-Time', a declarative representational and computational framework to perform commonsense reasoning about regions with both spatial and temporal components. Supported are capabilities for mixed qualitative-quantitative reasoning, consistency checking, and inferring compositions of space-time relations; these capabilities combine and synergise for applications in a range of AI application areas where the processing and interpretation of spatio-temporal data is crucial. The framework and resulting system is the only general KR-based method for declaratively reasoning about the dynamics of space-time' regions as first-class objects. We present an empirical evaluation (with scalability and robustness results), and include diverse application examples involving interpretation and control tasks.
@cite_5: We present a new approach to integrating Constraint Processing (CP) techniques into Answer Set Programming (ASP). Based on an alternative semantic approach, we develop an algorithmic framework for conflict-driven ASP solving that exploits CP solving capacities. A significant technical issue concerns the combination of conflict information from different solver types. We have implemented our approach, combining ASP solver clingo with the generic CP solver gecode , and we empirically investigate its computational impact.
@cite_17: The systematic modelling of [9] is a key requirement in a wide range of application areas such as comonsense cognitive robotics, computer-aided architecture design, dynamic geographic information systems. We present ASPMT(QS), a novel approach and fully-implemented prototype for non-monotonic spatial reasoning ---a crucial requirement within dynamic spatial systems-- based on Answer Set Programming Modulo Theories (ASPMT). ASPMT(QS) consists of a (qualitative) spatial representation module (QS) and a method for turning tight ASPMT instances into Sat Modulo Theories (SMT) instances in order to compute stable models by means of SMT solvers. We formalise and implement concepts of default spatial reasoning and spatial frame axioms using choice formulas. Spatial reasoning is performed by encoding spatial relations as systems of polynomial constraints, and solving via SMT with the theory of real nonlinear arithmetic. We empirically evaluate ASPMT(QS) in comparison with other prominent contemporary spatial reasoning systems. Our results show that ASPMT(QS) is the only existing system that is capable of reasoning about indirect spatial effects (i.e. addressing the ramification problem), and integrating geometric and qualitative spatial information within a non-monotonic spatial reasoning context.
ASP Modulo extensions for handling specialised domains and abstraction mechanisms provides a powerful means for the utilising ASP as a foundational knowledge representation and reasoning (KR) method for a wide-range of application contexts. This approach is clearly demonstrated in work such as ASPMT , @cite_5 , ASPMT(QS) . Most closely related to our research is the ASPMT founded system @cite_17 . Whereas provides a valuable blueprint for the integration and formulation of geometric and spatial reasoning within answer set programming modulo theories, the developed system is a first-step and lacks support for a rich spatio-temporal ontology or an elaborate characterisation of complex space-time' objects as native (the focus there has been on enabling non-monotonicity with a basic spatial and temporal ontology). In addition to the ontological extensions for a much richer space-time' component, our system pipeline --based on --- has the following additional advantages over the standard ASPMT ASPMT( ) pipeline: . we generate spatially consistent models compared to only one model in the standard ASPMT pipeline; . we compute optimal answer sets, e.g. add support preferences, which allows us to rank models, specify weak constraints; . unlike ASPMT( ) we support quantification of space-time regions.