Multi-agent planning

Abstract: Loosely coupled multi-agent systems are perceived as easier to plan for because they require less coordination between agent sub-plans. In this paper we set out to formalize this intuition. We establish an upper bound on the complexity of multi-agent planning problems that depends exponentially on two parameters quantifying the level of agents' coupling, and on these parameters only. The first parameter is problem-independent, and it measures the inherent level of coupling within the system. The second is problem-specific and it has to do with the minmax number of action-commitments per agent required to solve the problem. Most importantly, the direct dependence on the number of agents, on the overall size of the problem, and on the length of the agents' plans, is only polynomial. This result is obtained using a new algorithmic methodology which we call "planning as CSP+planning". We believe this to be one of the first formal results to both quantify the notion of agents' coupling, and to demonstrate a multi-agent planning algorithm that, for fixed coupling levels, scales polynomially with the size of the problem.

Abstract: A method for synthesizing multi-agent plans from simpler single-agent plans is described. The idea is to insert communication acts into the single-agent plans so that agents can synchronize activities and avoid harmful interactions. Unlike most previous planning systems, actions are represented by sequences of states, rather than as simple state change operators. This allows the expression of more complex kinds of interaction than would otherwise be possible. An efficient method of interaction and safety analysis is then developed and used to identify critical regions in the plans. An essential feature of the method is that the analysis is performed without generating all possible interleavings of the plans, thus avoiding a combinatorial explosion. Finally, communication primitives are inserted into the plans and a supervisor process created to handle synchronization.

Abstract: This paper describes a framework for automatically generating optimal action-level behavior for a team of robots based on temporal logic mission specifications under resource constraints. The propo...