Integrating knowledge in problem solving search procedures

ACM '84 Pub Date : 1900-01-01 DOI:10.1145/800171.809596
Vipin Kumar
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引用次数: 0

Abstract

With the help of a model for discrete optimization problems, we show that a large number of heuristic search procedures (for searching state-space graphs, AND/OR graphs, game trees, etc.) of artificial intelligence (AI), and dynamic programming (DP) and branch-and-bound (B&B) procedures of operations research use problem-specific knowledge in a framework based upon context-free grammar. The model reveals the true nature of these procedures, and aids in synthesizing new variations as well as generalizations and parallel implementations of these procedures. The paper concludes by commenting upon how this model may be generalized and made more powerful to encompass a greater variety of problems, and to help synthesize more efficient search procedures.
将知识整合到解决问题的搜索程序中
在离散优化问题模型的帮助下,我们展示了人工智能(AI)的大量启发式搜索过程(用于搜索状态空间图,AND/OR图,游戏树等)以及运筹学的动态规划(DP)和分支定界(B&B)过程在基于上下文无关语法的框架中使用特定于问题的知识。该模型揭示了这些过程的真实本质,并有助于综合这些过程的新变化以及概括和并行实现。本文最后评论了如何将该模型推广并使其更强大,以包含更多种类的问题,并帮助合成更有效的搜索过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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