AI Communications - Special issue on: Spatial and temporal reasoning
AI Communications - Spatial and Temporal Reasoning
Retrieving the most probable solution in a temporal interval algebra network
EC'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Evolutionary Computing - Volume 8
Finding the most probable solution to a probabilistic temporal interval algebra network
NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
Modelling and solving temporal reasoning as propositional satisfiability
Artificial Intelligence
Towards an efficient SAT encoding for temporal reasoning
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Conditional and composite temporal CSPs
Applied Intelligence
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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Local search techniques have attracted considerable interest in the artificial intelligence community since the development of GSAT and the min-conflicts heuristic for solving propositional satisfiability (SAT) problems and binary constraint satisfaction problems (CSPs) respectively. Newer techniques, such as the discrete Langrangian method (DLM), have significantly improved on GSAT and can also be applied to general constraint satisfaction and optimization. However, local search has yet to be successfully employed in solving temporal constraint satisfaction problems (TCSPs). This paper argues that current formalisms for representing TCSPs are inappropriate for a local search approach, and proposes an alternative CSP-based end-point ordering model for temporal reasoning. The paper looks at modelling and solving problems formulated using Allen's interval algebra (IA) and proposes a new constraint weighting algorithm derived from DLM. Using a set of randomly generated IA problems, it is shown that local search outperforms existing consistency-enforcing algorithms on those problems that the existing techniques find most difficult.