Compiling constraint satisfaction problems

Many tasks requiring intelligence, in particular scheduling and planning, must be solved under time constraints. This is difficult to achieve because of the combinatorial nature of such tasks. While search heuristics can give good average performance, they cannot give any performance guarantees for a particular instance. Fortunately, the tasks are often very similar. Therefore, compiling partial solutions is one way in which better performance guarantees for on-line problem solving could be achieved. We consider constraint satisfaction as a general paradigm and describe compilation techniques. General tasks are defined by incomplete CSPs from which instances are generated by adding more constraints. For any such general task, compilation builds a structure which represents all its solutions. in order to represent the space in a compact form, it exploits clustering and interchangeability techniques. Search for solutions can then be Limited to a usually much smaller, precomputed space. When search criteria involve only single variables, solutions can be guaranteed to be found in linear time in the size of the compiled structure. (C) 1999 Elsevier Science B.V. All rights reserved.