From Public Plans to Global Solutions in Multiagent Planning

Abstract

Multiagent planning addresses the problem of coordinated sequential decision making of a team of cooperative agents. One possible approach to multiagent planning, which proved to be very efficient in practice, is to find an acceptable public plan. The approach works in two stages. At first, a public plan acceptable to all the involved agents is computed. Then, in the second stage, the public solution is extended to a global solution by filling in internal information by every agent. In the recently proposed distributed multiagent planner, the winner of the Competition of Distributed Multiagent Planners (CoDMAP 2015), this principle was utilized, however with unnecessary use of combination of both public and internal information for extension of the public solution.

In this work, we improve the planning algorithm by enhancements of the global solution reconstruction phase. We propose a new method of global solution reconstruction which increases efficiency by restriction to internal information. Additionally, we employ reduction techniques downsizing the input planning problem. Finally, we experimentally evaluate the resulting planner and prove its superiority when compared to the previous approach.