Domain-independent multi-agent plan repair

Achieving joint objectives in distributed domain-independent planning problems by teams of cooperative agents requires significant coordination and communication efforts. For systems facing a plan failure in a dynamic environment, arguably, attempts to repair the failed plan in general, and especially in the worst-case scenarios, do not straightforwardly bring any benefit in terms of time complexity. However, in multi-agent settings, the communication complexity might be of a much higher importance, possibly a high communication overhead might be even prohibitive in certain domains. We hypothesize that in decentralized systems, where frequent coordination is required to achieve joint objectives, attempts to repair failed multi-agent plans should lead to lower communication overhead than replanning from scratch. Here, we formally introduce the multi-agent plan repair problem. Building upon the formal treatment, we present the core hypothesis underlying our work and subsequently describe three algorithms for multi-agent plan repair reducing the problem to specialized instances of the multi-agent planning problem. Finally, we present an experimental validation, results of which confirm the core hypothesis of the paper. Our rigorous treatment of the problem and experimental results pave the way for both further analytical, as well algorithmic investigations of the problem.

[1]  Michal Pechoucek,et al.  Decentralized multi-agent plan repair in dynamic environments , 2012, AAMAS.

[2]  Bernhard Nebel,et al.  The FF Planning System: Fast Plan Generation Through Heuristic Search , 2011, J. Artif. Intell. Res..

[3]  Hector Muñoz-Avila,et al.  On the Complexity of Plan Adaptation by Derivational Analogy in a Universal Classical Planning Framework , 2002, ECCBR.

[4]  Patrick Prosser,et al.  HYBRID ALGORITHMS FOR THE CONSTRAINT SATISFACTION PROBLEM , 1993, Comput. Intell..

[5]  Michael Rovatsos,et al.  Scaling Up Multiagent Planning: A Best-Response Approach , 2011, ICAPS.

[6]  Edmund H. Durfee,et al.  An efficient algorithm for multiagent plan coordination , 2005, AAMAS '05.

[7]  Hector Muñoz-Avila,et al.  Case-Based Plan Adaptation: An Analysis and Review , 2008, IEEE Intelligent Systems.

[8]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[9]  Wojciech Jamroga,et al.  Agents, Actions and Goals in Dynamic Environments , 2011, IJCAI.

[10]  Peter Novák,et al.  Multi-agent Plan Repairing ∗ , .

[11]  Amnon Meisels,et al.  Asynchronous Forward-checking for DisCSPs , 2007, Constraints.

[12]  Bernhard Nebel,et al.  Plan Reuse Versus Plan Generation: A Theoretical and Empirical Analysis , 1995, Artif. Intell..

[13]  Ivan Serina,et al.  Plan Stability: Replanning versus Plan Repair , 2006, ICAPS.

[14]  Christian J. Muise,et al.  Monitoring the Execution of Partial-Order Plans via Regression , 2011, IJCAI.

[15]  Hector Geffner,et al.  Compiling Uncertainty Away in Non-Deterministic Conformant Planning , 2010, ECAI.

[16]  Ronen I. Brafman,et al.  A general, fully distributed multi-agent planning algorithm , 2010, AAMAS.

[17]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[18]  Hector Geffner,et al.  A Translation-Based Approach to Contingent Planning , 2009, IJCAI.

[19]  NebelBernhard,et al.  The FF planning system , 2001 .

[20]  van der R.P.J. Krogt,et al.  Self-interested Planning Agents using Plan Repair , 2005 .

[21]  Ronen I. Brafman,et al.  From One to Many: Planning for Loosely Coupled Multi-Agent Systems , 2008, ICAPS.

[22]  J. Glinsky,et al.  The general. , 1982, Nursing.

[23]  Roman Barták,et al.  Constraint Processing , 2009, Encyclopedia of Artificial Intelligence.