Integrating Constraint Satisfaction and Spatial Reasoning

Many problems in AI, including planning, logical reasoning and probabilistic inference, have been shown to reduce to (weighted) constraint satisfaction. While there are a number of approaches for solving such problems, the recent gains in efficiency of the satisfiability approach have made SAT solvers a popular choice. Modern propositional SAT solvers are efficient for a wide variety of problems. However, particularly in the case of spatial reasoning, conversion to propositional SAT can sometimes result in a large number of variables and/or clauses. Moreover, spatial reasoning problems can often be more efficiently solved if the agent is able to exploit the geometric nature of space to make better choices during search and backtracking. The result of these two drawbacks — larger problem sizes and inefficient search — is that even simple spatial constraint problems are often intractable in the SAT approach. In this paper we propose a spatial reasoning system that provides significant performance improvements in constraint satisfaction problems involving spatial predicates. The key to our approach is to integrate a diagrammatic representation with a DPLL-based backtracking algorithm that is specialized for spatial reasoning. The resulting integrated system can be applied to larger and more complex problems than current approaches and can be adopted to improve performance in a variety of problems ranging from planning to probabilistic inference