Nicholas Cassimatis

AAAI Conference 2010 Conference Paper

Integrating Constraint Satisfaction and Spatial Reasoning

• Unmesh Kurup
• Nicholas Cassimatis

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.

IS Journal 2009 Journal Article

Flexible Inference with Structured Knowledge through Reasoned Unification

• Nicholas Cassimatis

AAAI Conference 2007 Conference Paper

An Architecture for Adaptive Algorithmic Hybrids

• Nicholas Cassimatis
• Scott Dugas

We describe a cognitive architecture for creating more robust intelligent systems by executing hybrids of algorithms based on different computational formalisms. The architecture is motivated by the belief that (1) most existing computational methods often exhibit some of the characteristics desired of intelligent systems at the cost of other desired characteristics and (2) a system exhibiting robust intelligence can be designed by implementing hybrids of these computational methods. The main obstacle to this approach is that the various relevant computational methods are based on data structures and algorithms that are very difficult to integrate into one system. We describe a new method of executing hybrids of algorithms using the focus of attention of multiple modules. This approach has been embodied in the Polyscheme cognitive architecture. Systems based on Polyscheme can integrate reactive robotic controllers, logical and probabilistic inference algorithms, frame-based formalisms and sensor-processing algorithms into one system. Existing applications involve human-robot interaction, heterogeneous information retrieval and natural language understanding. Systems built using Polyscheme demonstrate that algorithmic hybrids implemented using a focus of attention can (1) exhibit more characteristics of intelligence than individual computational methods alone and (2) deal with problems that have formerly been beyond the reach of synthetic computational intelligence.