Search in Artificial Intelligence

Search is an important component of problem solving in artificial intelligence (AI) and, more generally, in computer science, engineering and operations research. Combinatorial optimization, decision analysis, game playing, learning, planning, pattern recognition, robotics and theorem proving are some of the areas in which search algbrithms playa key role. Less than a decade ago the conventional wisdom in artificial intelligence was that the best search algorithms had already been invented and the likelihood of finding new results in this area was very small. Since then many new insights and results have been obtained. For example, new algorithms for state space, AND/OR graph, and game tree search were discovered. Articles on new theoretical developments and experimental results on backtracking, heuristic search and constraint propaga­ tion were published. The relationships among various search and combinatorial algorithms in AI, Operations Research, and other fields were clarified. This volume brings together some of this recent work in a manner designed to be accessible to students and professionals interested in these new insights and developments.

1. The CDP: A Unifying Formulation for Heuristic Search, Dynamic Programming, and Branch-and-Bound.- 2. An Algebra for Search Problems and Their Solutions.- 3. A General Branch-and-Bound Formulation for AND/OR Graph and Game Tree Search.- 4. Average-Case Analysis of Heuristic Search in Tree-Like Networks.- 5. The Optimality of A*.- 6. Network Search Algorithms with Modifiable Heuristics.- 7. Optimal Path Finding Algorithms.- 8. Developments with GPS.- 9. Tree Search and Arc Consistency in Constraint Satisfaction Algorithms.- 10. Backtrack-Free and Backtrack-Bounded Search.- 11. Network-Based Heuristics for Constraint-Satisfaction Problems.- 12. Fundamental Properties of Networks of Constraints: A New Formulation.- 13. Comparison of the MINIMAX and PRODUCT Back-Up Rules in a Variety of Games.