A graph based backtracking algorithm for solving general CSPs

  • Authors:

  • Wanlin Pang;Scott D. Goodwin

  • Affiliations:

  • QSS Group Inc., NASA Ames Research Center, Moffett Field, CA;School of Computer Science, University of Windsor, Windsor, Ontario, Canada

  • Venue:
  • AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
  • Year:
  • 2003

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Abstract

Many AI tasks can be formalized as constraint satisfaction problems (CSPs), which involve finding values for variables subject to constraints. While solving a CSP is an NP-complete task in general, tractable classes of CSPs have been identified based on the structure of the underlying constraint graphs. Much effort has been spent on exploiting structural properties of the constraint graph to improve the efficiency of finding a solution. These efforts contributed to development of a class of CSP solving algorithms called decomposition algorithms. The strength of CSP decomposition is that its worst-case complexity depends on the structural properties of the constraint graph and is usually better than the worst-case complexity of search methods. Its practical application is limited, however, since it cannot be applied if the CSP is not decomposable. In this paper, we propose a graph based backtracking algorithm called ω-CDBT, which shares merits and overcomes the weaknesses of both decomposition and search approaches.