F. Kabanza, S. Lu, and S. Goodwin (Canada)
Task planning, distributed planning, distributed search
A wide range of planning applications are combinatorial in nature, making the design of general purpose planning algorithms a still very challenging endeavor. In order to cope with this combinatorial complexity, some of the most recent work in artificial intelligence (AI) planning focuses on the use of sophisticated heuristics, domain search control knowledge, random search and efficient abstract state space encodings such as binary decision diagrams. The additional performance needed by complex planning applications can be provided by adopting massively parallel computing systems, such as networks of clusters. This paper describes a simple, general approach for turning backtrack search based planners into more powerful distributed systems that run on networks of clusters. Our approach consists in distributing backtrack search points to different processes on the network. We illustrate its potential using DSHOP, a distributed version of the SHOP planner.
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