Citing the confusing statements in the AI literature concerning the relationship between branch and bound (B&B) and heuristic search procedures were present a simple and general formulation of B&B which should help dispel much of the confusion. We illustrate the utility of the formulation by showing that through it some apparently very different algorithms for searching And/Or trees reveal the specific nature of their similarities and differences. In addition to giving new insights into the relationships among some AI search algorithms, the general formulation also provides suggestions on how existing search procedures may be varied to obtain new algorithms.

A new procedure is presented for growing min-max game trees. In large games, such as chess, decisions must be based on incomplete search trees. The new tree-growth procedure is based on “conspiracy numbers” as a measure of the accuracy of the root minimax value of an incomplete tree. Conspiracy numbers measure the number of leaf nodes whose value must change in order to change the minimax root value by a given amount. Trees are grown in a way that maximizes the conspiracy required to change the root value.

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In Mora, T. (Ed.), Applied Algebra, Algebraic Algorithms and Error-Correcting Codes, pp. 99–110. Springer-Verlag.

As a book about search, it is thorough, at the state of the art, and contains expositions that will delight the expert with their clarity and depth. However, it is not, per se, a book about AI (nor was it intended to be) or about the history, philosophy, or cognitive aspects of heuristic knowledge.

To fully appreciate Professor Pearl's book, begin with a and the numerous techniques for representing knowledge careful reading of the title. It is a book about "..Intelligent-and uncertainty in common use in mainstream AI. ..Strategies.." for the discovery and use of "Heuristics.. " Chapter 5 begins a quantitative performance analysis of to allow computers to solve ".. Search.. ' ' problems. This includes a nice exposition on is a critical component in AI programs (Nilsson 1980, Barr branching processes, although the mathematically unsophisticated and Feigenbaum 1982), and in this sense Pearl's book is a reader may find it difficult. Here Pearl introduces strong contribution to the field of AI. It serves as an excellent probabilistic models to complement probabilistic heuristics. As a book about search, it is thorough, at analysis of search heuristics, and to a probabilistic analysis the state of the art, and contains expositions that will delight of nonadmissible heuristics in ...

Proc. Tenth Intl. Joint Conference on Artificial Intelligence (pp. 334-337). Milan, Italy: IJCAII.

Annual Review of Computer Science

IEEE Computer, pages 100-109, January

Review of Heuristics: Intelligent Search Strategies for Computer Problem Solving