基于静态前提的谓词知识树分解策略

智能规划问题实质是一种搜索问题,通常需采用某种策略来缩小搜索空间,提高规划效率。在“以谓词为主体”的规划求解方法中,规划树的生成效率将直接影响规划求解效率。为此,提出了基于静态前提的谓词知识树分解策略,并给出了相应的分解算法。对任意一个规划领域,利用该分解算法可将知识树分解成若干个较小规模的知识子树。在规划求解的过程中,利用知识子树可有效地减少搜索空间,从而快速生成规划树,提高规划效率。同时,利用知识子树还可提取出隐含在动作描述中的领域知识。实验结果表明该分解算法是有效的。

Decomposition Strategy for Knowledge Tree of Predicate Based on Static Preconditions

AI planning is essentially a search problem,usually using some strategies to reduce the search space,improving planning efficiency.In the planning method of taking the predicate as target,the efficiency of planning tree generation will affect planning efficiency directly.Therefore,this paper proposesd a decomposition strategy for knowledge tree of predicate based on the static preconditions,and gave the corresponding decomposition algorithm.For any domain,using the algorithm,knowledge tree of predicate can be decomposed into a number of smaller knowledge sub-trees.The use of knowledge sub-trees in planning process can reduce the search space effectively,thus generating plan tree quickly and improving the planning efficiency.At the same time,the domain knowledge is extracted from the domain with them.Finally,the experiment results show that decomposition algorithm is efficient.