Trees have played a key role in the study of constraint satisfaction problems because problems with tree structure can be solved efficiently. It is shown here that a family of generalized trees, k-trees, can offer increasing representational complexity for constraint satisfaction problems, while maintaining a bound on computational complexity linear in the number of variables and exponential in k. Additional results are obtained for larger classes of graphs known as partial k-trees. These methods may be helpful even when the original problem does not have k-tree or partial k-tree structure. Specific tradeoffs are suggested between representational power and computational complexity.
Several content-based queries in spatial databases and geographic information systems (GISs) can be modelled and processed as constraint satisfaction problems (CSPs). Regular CSP algorithms, however, work for main memory retrieval without u tilizing indices to prune the search space. This paper shows how systematic and local search techniques can take advantage of the hierarchical decomposition of space, preserved by spatial data structures, to efficiently guide search. We study the cond itions under which hierarchical constraint satisfaction outperforms traditional methods with extensive experimentation.
Many design problems can be formulated as a process of searching a "well-defined" space of artifacts with similar functionality. The dimensions of such spaces are largely known and are constrained by relations obtained from the implicit functionality of the designed artifact. After identifying the kinds of knowledge that mediate the search for acceptable designs, a computational framework is presented that organizes the required knowledge as design plans. A problem solver is described that executes these plans. The problem solver extends the notion of dependency-directed backtracking with an advice mechanism. This mechanism allows information from a constraint failure to be used as advice in modifying a partial design. An expert system for designing paper transports inside copiers has been successfully built based on this framework.
This paper describes a flexible framework and an efficient algorithm for constraint-based spatiotemporal configuration problems. Binary constraints between spatiotemporal objects are first converted to constraint regions, which are then decomposed into hierarchical data structures; based on this constraint decomposition, an improved backtracking algorithm called HBT can compute a solution quite efficiently. In contrast to other approaches, the proposed method is characterized by the efficient handling of arbitrarily-shaped objects, and the flexible integration of quantitative and qualitative constraints; it allows a wide range of objects and constraints to be utilized for specifying a spatiotemporal configuration. The method is intended primarily for configuration problems in user interfaces, but can effectively be applied to similar problems in other areas as well. Introductio Spatiotemporal configuration problems based on constraints arise in many important application areas of artificial intelligence, such as planning, robotics and user interfaces.
Meta-S is a practical implementation and extension of the theoretical framework developed by Hofstedt, which allows the user to attack problems requiring the cooperation of arbitrary domain-specific constraint solvers. Through its modular structure and its extensible strategy specification language it also serves as a test-bed for generic and problem-specific (meta-)solving strategies, which are employed to minimize the cooperation overhead incurred. This paper introduces Meta-S, focusing on its strategy-related aspects.