Spatial Symmetry Driven Pruning Strategies for Efficient Declarative Spatial Reasoning
Carl Schultz, Mehul Bhatt

TL;DR
This paper introduces a novel symmetry-based pruning algorithm for declarative spatial reasoning that significantly enhances computational efficiency, enabling practical solutions to complex geometric and qualitative spatial problems.
Contribution
The paper presents a new symmetry-driven pruning strategy integrated into CLP(QS) that drastically improves spatial reasoning performance over traditional polynomial constraint methods.
Findings
Outperforms conventional polynomial encodings by orders of magnitude
Enables solving previously intractable spatial reasoning problems
Effective across contact, mereology, and geometry benchmarks
Abstract
Declarative spatial reasoning denotes the ability to (declaratively) specify and solve real-world problems related to geometric and qualitative spatial representation and reasoning within standard knowledge representation and reasoning (KR) based methods (e.g., logic programming and derivatives). One approach for encoding the semantics of spatial relations within a declarative programming framework is by systems of polynomial constraints. However, solving such constraints is computationally intractable in general (i.e. the theory of real-closed fields). We present a new algorithm, implemented within the declarative spatial reasoning system CLP(QS), that drastically improves the performance of deciding the consistency of spatial constraint graphs over conventional polynomial encodings. We develop pruning strategies founded on spatial symmetries that form equivalence classes (based on…
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Taxonomy
TopicsConstraint Satisfaction and Optimization · Logic, Reasoning, and Knowledge · Semantic Web and Ontologies
