An Attempt to Map the Performance of a Range of Algorithm and Heuristic Combinations

Constraint satisfaction is the core of many AI and real life problems and much research has been done in this field in recent years. Work has been done in the past in comparing the performance of different algorithms and heuristics. Much of such work has focused on finding "the best" algorithm and heuristic combination for all problems. The objective of this paper is to prove that there is no universally best algorithm and heuristic for all problems -different problems can be solved most efficiently by different algorithm and heuristic combinations. The implication of this is important because it means that instead of trying to find "the best" algorithms and heuristics, future research should try to identify the application domain of each algorithm and heuristic (i.e. when they are most effective). Furthermore our results point to future research which focuses on how to retrieve the most efficient algorithm for a given problem. The results in this paper provide a first step towards achieving such goals. * The research reported in this paper was supported by the University of Essex research promotion fund ref. R7027 and by the EPSRC research grant ref. GR/J/42878.

[1]  Andrew B. Baker,et al.  The Hazards of Fancy Backtracking , 1994, AAAI.

[2]  Bernard A. Nadel,et al.  Constraint satisfaction algorithms 1 , 1989, Comput. Intell..

[3]  Bernard Nudel,et al.  Consistent-Labeling Problems and Their Algorithms , 1982, AAAI.

[4]  Barbara M. Smith.  In Search of Exceptionally Difficult Constraint Satisfaction Problems , 1995, Constraint Processing, Selected Papers.

[5]  Thesis in preparation , 1985 .

[6]  Eugene C. Freuder,et al.  Contradicting Conventional Wisdom in Constraint Satisfaction , 1994, ECAI.

[7]  Alan K. Mackworth.  Consistency in Networks of Relations , 1977, Artif. Intell..

[8]  Eugene C. Freuder.  A Sufficient Condition for Backtrack-Free Search , 1982, JACM.

[9]  John Gaschnig,et al.  A General Backtrack Algorithm That Eliminates Most Redundant Tests , 1977, IJCAI.

[10]  Rina Dechter,et al.  Tree Clustering for Constraint Networks , 1989, Artif. Intell..

[11]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.

[12]  Bernard Nudel,et al.  Consistent-Labeling Problems and Their Algorithms: Expected-Complexities and Theory-Based Heuristics , 1983, Artif. Intell..

[13]  Rina Dechter,et al.  Experimental Evaluation of Preprocessing Techniques in Constraint Satisfaction Problems , 1989, IJCAI.

[14]  Peter van Beek,et al.  A Theoretical Evaluation of Selected Backtracking Algorithms , 1995, IJCAI.

[15]  Robert M. Haralick,et al.  Increasing Tree Search Efficiency for Constraint Satisfaction Problems , 1979, Artif. Intell..

[16]  Barbara M. Smith,et al.  The Phase Transition and the Mushy Region in Constraint Satisfaction Problems , 1994, ECAI.

[17]  Patrick Prosser,et al.  Domain Filtering can Degrade Intelligent Backtracking Search , 1993, IJCAI.

[18]  Rina Dechter,et al.  Network-based heuristics for constraint satisfaction problems , 1988 .

[19]  Patrick Prosser,et al.  Binary Constraint Satisfaction Problems: Some are Harder than Others , 1994, ECAI.

[20]  J. Gaschnig.  Performance measurement and analysis of certain search algorithms. , 1979 .

[21]  Patrick Prosser,et al.  HYBRID ALGORITHMS FOR THE CONSTRAINT SATISFACTION PROBLEM , 1993, Comput. Intell..

[22]  Rina Dechter,et al.  Experimental Evaluation of Preprocessing Algorithms for Constraint Satisfaction Problems , 1994, Artif. Intell..

[23]  Peter C. Cheeseman,et al.  Where the Really Hard Problems Are , 1991, IJCAI.

[24]  Christian Bessiere,et al.  Arc-Consistency and Arc-Consistency Again , 1993, Artif. Intell..