Systematic versus non systematic techniques for solving temporal constraints in a dynamic environment

Issue title: Spatial and Temporal Reasoning

Article type: Research Article

Authors: Mouhoub, Malek

Affiliations: Department of Computer Science, University of Regina, 3737 Wascana Parkway, Regina SK, Canada, S4S 0A2 E‐mail: [email protected]

Abstract: A main challenge when designing constraint based systems in general and those involving temporal constraints in particular, is the ability to deal with constraints in a dynamic and evolutive environment. That is to check, anytime a new constraint is added, whether a consistent scenario continues to be consistent when a new constraint is added and if not, whether a new scenario satisfying the old and new constraints can be found. We talk then about on line temporal constraint based systems capable of reacting, in an efficient way, to any new external information during the constraint resolution process. In this paper, we will investigate the applicability of systematic versus approximation methods for solving incremental temporal constraint problems. In order to handle both numeric and symbolic constraints, the systematic method is based on constraint propagation performed at both the qualitative and quantitative levels. The approximation methods are respectively based on stochastic local search and genetic algorithms. Experimental evaluation of the performance in time and the quality of the solution returned (number of violated constraints) of the different techniques has been performed on randomly generated temporal constraint problems. The results favour the exact method for problems with reasonable size while the approximation techniques are the methods of choice for very large problems in the case where we want to trade the quality of the solution for the process time. Indeed, while the approximation methods are faster for large problems, they do not guarantee, in general, the completeness of the solution returned.

Keywords: Temporal reasoning, constraint propagation, stochastic local search, genetic algorithms

Journal: AI Communications, vol. 17, no. 4, pp. 201-211, 2004