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FMAP: Distributed cooperative multi-agent planning

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Abstract

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.

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Notes

  1. http://en.wikipedia.org/wiki/Planning_Domain_Definition_Language

  2. http://ipc.icaps-conference.org/

  3. http://www.gti-ia.upv.es/sma/tools/magentix2

  4. http://qpid.apache.org/

References

  1. Benton J, Coles A, Coles A (2012) Temporal planning with preferences and time-dependent continuous costs. In: Proceedings of the 22nd international conference on automated planning and scheduling (ICAPS). AAAI, pp 2–10

  2. Borrajo D. (2013) Multi-agent planning by plan reuse. In: Proceedings of the 12th international conference on autonomous agents and multi-agent systems (AAMAS). IFAAMAS, pp 1141–1142

  3. Boutilier C, Brafman R (2001) Partial-order planning with concurrent interacting actions. J Artif Intell Res 14(105):136

    Google Scholar 

  4. Brafman R, Domshlak C (2008) From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the 18th international conference on automated planning and scheduling (ICAPS). AAAI, pp 28–35

  5. Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiagent Syst 19(3):297–331

    Article  Google Scholar 

  6. Bresina J, Dearden R, Meuleau N, Ramakrishnan S, Smith D, Washington R (2002) Planning under continuous time and resource uncertainty: a challenge for AI. In: Proceedings of the 18th conference on uncertainty in artificial intelligence (UAI). Morgan Kaufmann, pp 77–84

  7. Cox J, Durfee E (2009) Efficient and distributable methods for solving the multiagent plan coordination problem. Multiagent Grid Syst 5(4):373–408

    MATH  Google Scholar 

  8. Crosby M, Rovatsos M, Petrick R (2013) Automated agent decomposition for classical planning. In: Proceedings of the 23rd international conference on automated planning and scheduling (ICAPS). AAAI, pp 46–54

  9. Dimopoulos Y, Hashmi MA, Moraitis P (2012) μ-satplan: Multi-agent planning as satisfiability. Knowl-Based Syst 29:54–62

    Article  Google Scholar 

  10. Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208

    Article  MATH  Google Scholar 

  11. Gerevini A, Haslum P, Long D, Saetti A, Dimopoulos Y (2009) Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners. Artif Intell 173(5-6):619–668

    Article  MATH  MathSciNet  Google Scholar 

  12. Ghallab M, Nau D, Traverso P (2004) Automated planning. Theory and practice. Morgan Kaufmann

  13. Günay A, Yolum P (2013) Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments. Appl Intell 39(3):489–509

    Article  Google Scholar 

  14. Helmert M (2004) A planning heuristic based on causal graph analysis. In: Proceedings of the 14th international conference on automated planning and scheduling ICAPS. AAAI, pp 161–170

  15. Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302

    MATH  Google Scholar 

  16. Jannach D, Zanker M (2013) Modeling and solving distributed configuration problems: a CSP-based approach. IEEE Trans Knowl Data Eng 25(3):603–618

    Article  Google Scholar 

  17. Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 114–121

  18. Kala R, Warwick K (2014) Dynamic distributed lanes: motion planning for multiple autonomous vehicles. Appl Intell:1–22

  19. Koehler J, Ottiger D (2002) An AI-based approach to destination control in elevators. AI Mag 23(3):59–78

    Google Scholar 

  20. Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical report

  21. van der Krogt R (2009) Quantifying privacy in multiagent planning. Multiagent Grid Syst 5(4):451–469

    MATH  Google Scholar 

  22. Kvarnström J (2011) Planning for loosely coupled agents using partial order forward-chaining. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 138–145

  23. Lesser V, Decker K, Wagner T, Carver N, Garvey A, Horling B, Neiman D, Podorozhny R, Prasad M, Raja A et al (2004) Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton Agents Multi-Agent Syst 9(1–2):87–143

    Article  Google Scholar 

  24. Long D, Fox M (2003) The 3rd international planning competition: results and analysis. J Artif Intell Res 20:1–59

  25. Nissim R, Brafman R, Domshlak C (2010) A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 1323–1330

  26. O’Brien P, Nicol R (1998) FIPA - towards a standard for software agents. BT Tech J 16(3):51–59

    Article  Google Scholar 

  27. Öztürk P, Rossland K, Gundersen O (2010) A multiagent framework for coordinated parallel problem solving. Appl Intell 33(2):132–143

    Article  Google Scholar 

  28. Pal A, Tiwari R, Shukla A (2013) Communication constraints multi-agent territory exploration task. Appl Intell 38(3):357–383

    Article  Google Scholar 

  29. Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177

    MATH  Google Scholar 

  30. de la Rosa T, García-Olaya A, Borrajo D (2013) A case-based approach to heuristic planning. Appl Intell 39(1):184–201

    Article  Google Scholar 

  31. Sapena O, Onaindia E (2008) Planning in highly dynamic environments: an anytime approach for planning under time constraints. Appl Intell 29(1):90–109

    Article  Google Scholar 

  32. Sapena O, Onaindia E, Garrido A, Arangú M (2008) A distributed CSP approach for collaborative planning systems. Eng Appl Artif Intell 21(5):698–709

    Article  Google Scholar 

  33. Serrano E, Such J, Botía J, García-Fornes A (2013) Strategies for avoiding preference profiling in agent-based e-commerce environments. Appl Intell:1–16

  34. Smith D, Frank J, Jónsson A (2000) Bridging the gap between planning and scheduling. Knowl Eng Rev 15(1):47–83

    Article  Google Scholar 

  35. Such J, García-Fornes A, Espinosa A, Bellver J (2012) Magentix2: a privacy-enhancing agent platform. Eng Appl Artif Intell:96–109

  36. Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145

    Article  MATH  MathSciNet  Google Scholar 

  37. Torreño A, Onaindia E, Sapena O (2012) An approach to multi-agent planning with incomplete information. In: Proceedings of the 20th European conference on artificial intelligence (ECAI), vol 242. IOS Press, pp 762–767

  38. Torreño A, Onaindia E, Sapena O (2014) A flexible coupling approach to multi-agent planning under incomplete information. Knowl Inf Syst 38(1):141–178

    Article  Google Scholar 

  39. Van Der Krogt R, De Weerdt M (2005) Plan repair as an extension of planning. In: Proceedings of the 15th international conference on automated planning and scheduling (ICAPS). AAAI, pp 161–170

  40. de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiagent Grid Syst 5(4):345– 355

    Google Scholar 

  41. Yokoo M, Durfee E, Ishida T, Kuwabara K (1998) The distributed constraint satisfaction problem: formalization and algorithms. IEEE Trans Knowl Data Eng 10(5):673–685

    Article  Google Scholar 

  42. Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th international joint conference conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 798–805

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Acknowledgments

This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, the Valencian Prometeo project II/2013/019, and the FPI-UPV scholarship granted to the first author by the Universitat Polit`ecnica de Val`encia.

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Correspondence to Alejandro Torreño.

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Torreño, A., Onaindia, E. & Sapena, Ó. FMAP: Distributed cooperative multi-agent planning. Appl Intell 41, 606–626 (2014). https://doi.org/10.1007/s10489-014-0540-2

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