Parallel planning via the distribution of operators

作     者：Vrakas, D Refanidis, J Vlahavas, I 

作者机构：Aristotelian Univ Thessaloniki Dept Informat GR-54006 Thessaloniki Greece 

出 版 物：《JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE》 (人工智能实验与理论杂志)

年 卷 期：2001年第13卷第3期

页      面：211-226页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：AI planning parallel algorithms load balancing heuristic algorithms 

摘      要：This paper describes Operator Distribution Method for parallel Planning (ODMP), a parallelization method for efficient heuristic planning. The method innovates in that it parallelizes the application of the available operators to the current state and the evaluation of the successor states using the heuristic function. In order to achieve better load balancing and a lift in the scalability of the algorithm, the operator set is initially enlarged, by grounding the first argument of each operator. Additional load balancing is achieved through the reordering of the operator set, based on the expected amount of imposed work. ODMP is effective for heuristic planners, but it can be applied to planners that embody other search strategies as well. It has been applied to GRT, a domain-independent heuristic planner, and CL, a heuristic planner for simple logistics problems, and has been thoroughly tested on a set of logistics problems adopted from the AIPS-98 planning competition, giving quite promising results.