A Distributed CSP Approach for Collaborative Planning Systems Oscar Sapena, Eva Onaindia, Antonio Garrido, Marlene Arangu Universidad Politecnica de Valencia Camino de Vera s/n, 46022 - Valencia (Spain) Abstract Distributed or multi-agent planning extends classical AI planning to domains where several agents can plan and act together. There exist many recent developments in this discipline that range over different approaches for distributed planning algorithms, distributed plan execution processes or communication protocols among agents. One of the key issues about distributed planning is that it is the most appropriate way to tackle certain kind of planning problems, specially those where a centralized solving is unfeasible. In this paper we present a new planning framework aimed at solving planning problems in inherently distributed domains where agents have a collection of private data which cannot share with other agents. However, collaboration is required since agents are unable to accomplish its own tasks alone or, at least, can accomplish its tasks better when working with others. Our proposal motivates a new planning scheme based on a distributed heuristic search and a constraint programming resolution process. Key words: Distributed planning, Collaborative planning, Constraint programming 1. Introduction Distributed planning is the problem of finding a course of actions that will help a set of agents collectively satisfy certain desired goals. Due to an inherent distribution of re- sources such as knowledge and capability among the agents, an agent in a distributed planning system is unable to ac- complish its own tasks alone, or at least can accomplish its tasks better when working with others (Durfee, 2001). Distributed planning is still an open challenge, and there is an increasingly number of applications that can benefit from this research area: cooperative robotics (Wehowsky et al., 2005) (Sirin et al., 2004), composition of semantic web services (Wu et al. 2003), manufacturing systems (Hahndel et al., 1996), etc. The problem of constructing plans in a distributed envi- ronment has been approached from two different directions (desJardins et al., 1999): – One approach has begun with a focus on planning and how it can be extended into a distributed environment. This approach is usually referred to as cooperative dis- tributed planning because it places the problem of form- ing a competent plan as the ultimate objective. Although 1 Tel.: +34 963877007; fax: +34 963877359. E-mail addresses: osapena@dsic.upv.es (corresponding author), onaindia@dsic.upv.es, agarridot@dsic.upv.es, marangu@dsic.upv.es. in some cases the purpose of the agents is to form a cen- tral plan, more generally the purpose is that the dis- tributed parts of the developing plan will jointly execute in a coherent and effective manner. Thus, in cooperative distributed planning, agents typically exchange informa- tion about their plans, which they iteratively refine and revise until they fit together well. – The other approach has begun with an emphasis on the problem of controlling and coordinating the actions of multiple agents in a shared environment. This approach usually is referred to as negotiated distributed planning. From the perspective of an individual agent, the purpose of negotiating over planned activities is not to form good collective plans but to ensure that the agents local ob- jectives will be met by its plan when viewed in a global context. The emphasis is therefore not on cooperatively defining and searching the space of joint plans to find the best group plan, as in the previous approach, but on having an agent provide enough information to others to convince them to accommodate its preferences. The work presented in paper follows the cooperative dis- tributed planning approach since the main goal is to effi- ciently obtain a good collective plan. The literature cites many reasons for which cooperative distributed planning is an interesting approach to pursue. One of these reasons is to split the problem into smaller subproblems which are usu- ally easier to solve. This divide-and-conquer approach has Preprint submitted to Elsevier 24 January 2008