Distributed Graphplan

(Appears as a regular paper in the Proceedings of IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Washington D.C, IEEE Computer Society, Nov. 2002, pp. 138-145. 27 out of 84 submissions were accepted as regular papers leading to 32 acceptance rate.) Significant advances in plan synthesis under classical assumptions have occurred in last seven years. All such efficient planners are centralized planners. One very major development among these is the Graphplan planner. Its popularity is clear from its several efficient adaptations/extensions. Since several practical planning problems are solved in a distributed manner, it is important to adapt Graphplan to distributed planning. This involves dealing with significant challenges like decomposing goal and set of actions without losing completeness. We report two sound two-agent planners DGP (distributed Graphplan) and IGDGP (interaction graph-based DGP). Decomposition of goal and action set in DGP is carried out manually and that in IG-DGP is carried out automatically based on a new representation called interaction graphs. Our empirical evaluation shows that both these distributed planners are faster than Graphplan. IG-DGP is orders of magnitude faster than Graphplan. IG-DGP is significantly benefitted by the interaction graphs which allow decomposition of a problem into fully independent subproblems under certain conditions. IG-DGP is a hybrid planner in which a centralized planner processes a problem until it becomes separable into two independent subproblems that are passed to a distributed planner. This paper also shows that advances in centralized planning can significantly benefit distributed