Hybrid Multi-agent Planning

Abstract

AbstractAlthough several approaches have been constructed for multi-agent planning, solving large planning problems is still quite difficult. In this paper, we present a new approach that integrates landmark preprocessing technique in the context of hierarchical planning with multi-agent planning. Our approach uses Dependent and Independent clustering techniques to break up the planning problem into smaller clusters. These clusters are solved individually according to landmark information, then the obtained individual plans are merged according to the notion of fragments to generate a final solution plan. In hierarchical planning, landmarks are those tasks that occur in the decomposition refinements on every plan development path. Hierarchical landmark technique shows how a preprocessing step that extracts landmarks from a hierarchical planning domain and problem description can be used to prune the search space that is to be explored before actual search is performed. The methodologies in this paper have been implemented successfully, and we will present some experimental results that give evidence for the considerable performance increase gained through our system.KeywordsPlanning ProblemSolution PlanInitial PlanLocal LandmarkIndividual PlanThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.