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Multi-Agent Planning with Joint Actions Satyendra Singh Chouhan, Rajdeep Niyogi Department of Computer Science & Engineering, IIT Roorkee, INDIA [email protected], [email protected] MAP with Joint Actions Introduction • Multi agent planning(MAP): “Planning by and for multiple agents” • MAP Applications: Logistics, Supply Chain Management, Search and Exploration, Robotics, Video games, Healthcare etc. Joint actions in multi-agent planning raise two main issues • Specification (input PDDL) • Planning (Algorithm to handle joint actions scenario) Existing Approaches to handle joint actions at specification level. Input (MA-PDDL) For agent α1 to αn Output: Sequence of actions (set) MA-plan: (A1; A2; ….;AK) Ai: {act(α1), act(α2) … act(αn) } at time i MAP system Fig 1. Coordination between agents Fig2. Cooperation between agents :action push :parameter(?a1-agent ?b –box ?l- location) :precondition (and(at ?a1 ?l) (at ?b ?l)) :concurrent (and (push ?a2 ?b ?l)(not(= ?a1 ?a2)) :effect(not (at ?b ?l)) :joint action push :parameter(?a1-agent ?a2-agent ?b–box ?l-location) :precondition (and(at ?a1 ?l)(at ?a2 ?l)(at ?b ?l) (not(=?a1 ?a2))) :effect(not(at ?b ?l)) (Boutilier, C et al. 2001) [1] (Brafman, R. et al. 2014)[2] • The given specifications will only work when we have complete knowledge of the domain. Most importantly, number of agents require to perform a joint action( #2 for above push action specification). • Above specifications will only be useful, if the total number of agents require to perform a joint action is at most two. If there are objects that require more than two agents then the specification will be complex. Motivation • MAP is a broad area of research in Artificial intelligence. Therefore, depending on the type of problem different map approaches are used. • Joint actions come naturally in MAP. • There are different ways to view joint action. Consider following example to understand it. Example 1: Narrow Doorway Domain A B • No. of agents: 2 (A, B) • Action set {go(G), wait(W)} • Possible joint actions would be (G, G), (G, W), (W, G) and (W, W). However, a successful plan would be obtained when one agent waits and allows the other agent to pass through the gate. Example 2: Logistics Domain Airplane1 L2 L3 Dest City2 In this domain, at any point in time when a truck is being loaded at one location, another truck may be unloaded at another location. An instance of joint action would be the set of two concurrent actions represented as <load(Truck1, city1), unload(Truck2, city2)> Example 3: Box-pushing Domain In box-pushing domain (Figure 2), two or more agents may need to push a heavy box simultaneously i.e. agents have to perform the same action at the same time. Here, an instance of a joint action would be represented as <push(Agent1, box1), push(Agent2, box1)> • In addition, A joint action can be a sequence of two or more actions such that a desire effect would only be obtained if theses actions are performed in a particular sequence. • Existing multi-agent planners can solve MAP problems similar to example 1 and 2. However, no multi-agent exist that can solve problems similar to example 3. www.PosterPresentations.com MAPR MA-FD FMAP √ √ √ × √ √ × × × • In [3], single agent planning approach is used to handle Map problems with concurrent actions. In this each agent’s action is associated with subset of objects (including agent count). Then problem is transformed into single agent planning problem. We have performed some experiments using above approach with some assumptions using Blackbox planner. Sr No. No. of Agents 1 2 3 4 5 2 2 2 3 3 Problem Size (s, m, l) (4,2,0) (5,3,0) (6,2,0) (6,2,1) (8,4,2) Box Pushing Domain Solved? Yes Yes Yes Yes No Extended-Block World Domain Solved? Yes Yes No No No Truck2 City1 RESEARCH POSTER PRESENTATION DESIGN © 2012 Domains Loosely-coupled planning domains Tightly-coupled planning domains Tightly-coupled planning domains with joint actions Current research objectives Truck1 Source Existing Multi agent Planning Systems • Current state of the art multi-agent planners does not supports joint actions. • Designing a Distributed Multi-agent system that can solve planning problems with joint actions. • Analysis of complex planning domains that involve cooperation among agents. Future research interest • Identifying class of problems for which proposed planning system is complete. • Scope of learning in Multi-agent planning. References 1. Boutilier, C. and Brafman, R. I . 2001. Planning with concurrent interacting actions. Journal of Artificial Intelligence Research (JAIR), pages 105–136. 2. Brafman, R. I and Zoran U. 2014. Distributed Heuristic Forward Search for Multi-agent system, In Proceedings of 2nd ICAPS DMAP workshop, pages 1-7. 3. Crosby, M., Jonsson, A., and Rovatsos, M. 2014. A Single-Agent Approach to Multi-agent Planning. In ECAI 2014, page 237