From Public Plans to Global Solutions in Multiagent Planning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9571)


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.


Multiagent Planning Public Plan Public Solutions Planar Location Problems Plan-space Search 
These 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.



This research was supported by the Czech Science Foundation (grant no. 13-22125S) and by the Ministry of Education of the Czech Republic within the SGS project (no. SGS13/211/OHK3/3T/13). Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum, provided under the program “Projects of Large Infrastructure for Research, Development, and Innovations” (LM2010005), is greatly appreciated.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.FEE, Department of Computer Science, Agent Technology CenterCzech Technical University in PraguePragueCzech Republic

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