Underconstrained knowledge-intensive constraint satisfaction search

Abstract This paper describes a methodology to efficiently solve underconstrained knowledge-intensive constraint satisfaction problems (CSPs) where solutions are ordered by a cost measure. The methodology exploits a segmentation of the CSP imposed by the application domain. Constraints and variables are clustered into CSP segments which support local constraint processing and solution cost estimation. The methodology employs heuristic search to develop solutions in an incremental fashion. The goal of search is to find one or several of a set of 'satisficing', i.e. good enough, solutions. Solution cost is defined as distance to a normal solution. The heuristic evaluation function is non-admissible and favours depth-first search because finding a good solution is more important than finding the absolute best one. The value of the evaluation function of a search node may change during processing in response to results of exploring nearby nodes. It attempts to improve its estimate of the remaining path cost b...

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