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Learning-Based Diagnosis and Repair

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Artificial Intelligence (BNAIC 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 823))

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Abstract

This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus exploitation of the learned assessments.

Compared to collaborative multi-agent diagnosis, the proposed learning-based approach is not very efficient. However, it does not depend on collaboration with other agents. The proposed learning based diagnosis approach may therefore provide an incentive to collaborate in the execution of tasks, and in diagnosis if tasks are executed in a suboptimal way.

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Notes

  1. 1.

    In smart energy networks the tasks are the directions in which energy must flow.

References

  1. Bellman, R.: A markovian decision process. J. Math. Mech. 6, 679–684 (1957)

    MathSciNet  MATH  Google Scholar 

  2. Bernstein, D.S., Givan, R., Immerman, N., Zilberstein, S.: Sequential optimality and coordination in multiagent systems. Math. Oper. Res. 6(4), 819–840 (2002)

    Article  Google Scholar 

  3. Boutilier, C.: Sequential optimality and coordination in multiagent systems. In: IJCAI, pp. 478–485 (1999)

    Google Scholar 

  4. Console, L., Torasso, P.: Hypothetical reasoning in causal models. Int. J. Intell. Syst. 5, 83–124 (1990)

    MATH  Google Scholar 

  5. Console, L., Torasso, P.: A spectrum of logical definitions of model-based diagnosis. Comput. Intell. 7, 133–141 (1991)

    Article  Google Scholar 

  6. Davis, R.: Diagnostic reasoning based on structure and behaviour. Artif. Intell. 24, 347–410 (1984)

    Article  Google Scholar 

  7. de Jonge, F., Roos, N.: Plan-execution health repair in a multi-agent system. In: PlanSIG 2004 (2004)

    Google Scholar 

  8. de Jonge, F., Roos, N., Aldewereld, H.: MATES 2007. LNCS, vol. 4687. Springer, Heidelberg (2007)

    Book  Google Scholar 

  9. de Jonge, F., Roos, N., Aldewereld, H.: Temporal diagnosis of multi-agent plan execution without an explicit representation of time. In: BNAIC-07 (2007)

    Google Scholar 

  10. de Jonge, F., Roos, N., van den Herik, J.: Keeping plan execution healthy. In: Pěchouček, M., Petta, P., Varga, L.Z. (eds.) CEEMAS 2005. LNCS (LNAI), vol. 3690, pp. 377–387. Springer, Heidelberg (2005). https://doi.org/10.1007/11559221_38

    Chapter  Google Scholar 

  11. de Jonge, F., Roos, N., Witteveen, C.: Diagnosis of multi-agent plan execution. In: Fischer, K., Timm, I.J., André, E., Zhong, N. (eds.) MATES 2006. LNCS (LNAI), vol. 4196, pp. 86–97. Springer, Heidelberg (2006). https://doi.org/10.1007/11872283_8

    Chapter  Google Scholar 

  12. de Jonge, F., Roos, N., Witteveen, C.: Primary and secondary plan diagnosis. In: The International Workshop on Principles of Diagnosis, DX-06 (2006)

    Google Scholar 

  13. Howard, R.A.: Dynamic Programming and Markov Processes. MIT Press, New York (1960)

    MATH  Google Scholar 

  14. Kalech, M., Kaminka, G.A.: On the design of social diagnosis algorithms for multi-agent teams. In: IJCAI-03, pp. 370–375 (2003)

    Google Scholar 

  15. Kalech, M., Kaminka, G.A.: Diagnosing a team of agents: scaling-up. In: AAMAS 2005, pp. 249–255 (2005)

    Google Scholar 

  16. Kalech, M., Kaminka, G.A.: Towards model-based diagnosis of coordination failures. In: AAAI 2005, pp. 102–107 (2005)

    Google Scholar 

  17. Kalech, M., Kaminka, G.A.: On the design of coordination diagnosis algorithms for teams of situated agents. Artif. Intell. 171, 491–513 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  18. Kalech, M., Kaminka, G.A.: Coordination diagnostic algorithms for teams of situated agents: scaling up. Comput. Intell. 27(3), 393–421 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. de Kleer, J., Mackworth, A.K., Reiter, R.: Characterizing diagnoses and systems. Artif. Intell. 56, 197–222 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  20. de Kleer, J., Williams, B.C.: Diagnosing with behaviour modes. In: IJCAI 89, pp. 104–109 (1989)

    Google Scholar 

  21. Micalizio, R.: A distributed control loop for autonomous recovery in a multi-agent plan. In: Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, (IJCAI-09), pp. 1760–1765 (2009)

    Google Scholar 

  22. Micalizio, R.: Action failure recovery via model-based diagnosis and conformant planning. Comput. Intell. 29(2), 233–280 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  23. Micalizio, R., Torasso, P.: On-line monitoring of plan execution: a distributed approach. Knowl. Based Syst. 20, 134–142 (2007)

    Article  Google Scholar 

  24. Micalizio, R., Torasso, P.: Plan diagnosis and agent diagnosis in multi-agent systems. In: Basili, R., Pazienza, M.T. (eds.) AI*IA 2007. LNCS (LNAI), vol. 4733, pp. 434–446. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74782-6_38

    Chapter  Google Scholar 

  25. Micalizio, R., Torasso, P.: Team cooperation for plan recovery in multi-agent systems. In: Petta, P., Müller, J.P., Klusch, M., Georgeff, M. (eds.) MATES 2007. LNCS (LNAI), vol. 4687, pp. 170–181. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74949-3_15

    Chapter  Google Scholar 

  26. Micalizio, R., Torasso, P.: Monitoring the execution of a multi-agent plan: dealing with partial observability. In: Proceedings of the 18th European Conference on Artificial Intelligence (ECAI-08), pp. 408–412. IOS Press (2008)

    Google Scholar 

  27. Micalizio, R., Torasso, P.: Cooperative monitoring to diagnose multiagent plans. J. Artif. Intell. Res. 51, 1–70 (2014)

    MathSciNet  MATH  Google Scholar 

  28. Micalizio, R., Torta, G.: Explaining interdependent action delays in multiagent plans execution. Auton. Agent. Multi-Agent Syst. 30(4), 601–639 (2016)

    Article  Google Scholar 

  29. Raiman, O., de Kleer, J., Saraswat, V., Shirley, M.: Characterizing non-intermittent faults. In: AAAI 91, pp. 849–854 (1991)

    Google Scholar 

  30. Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32, 57–95 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  31. Roos, N., ten Teije, A., Witteveen, C.: Reaching diagnostic agreement in multi-agent diagnosis. In: AAMAS 2004, pp. 1254–1255 (2004)

    Google Scholar 

  32. Roos, N., Witteveen, C.: Diagnosis of plan execution and the executing agent. In: Furbach, U. (ed.) KI 2005. LNCS (LNAI), vol. 3698, pp. 161–175. Springer, Heidelberg (2005). https://doi.org/10.1007/11551263_14

    Chapter  Google Scholar 

  33. Roos, N., Witteveen, C.: Diagnosis of plan structure violations. In: Petta, P., Müller, J.P., Klusch, M., Georgeff, M. (eds.) MATES 2007. LNCS (LNAI), vol. 4687, pp. 157–169. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74949-3_14

    Chapter  Google Scholar 

  34. Roos, N., Witteveen, C.: Models and methods for plan diagnosis. J. Auton. Agents Multi-Agent Syst. 19, 30–52 (2008)

    Article  Google Scholar 

  35. Watkins, C.J.C.H.: Learning from delayed rewards. Ph.D. thesis Cambridge University (1989)

    Google Scholar 

  36. Watkins, C.J.C.H., Dayan, P.: Q-learning. Mach. Learn. 8(3), 279–292 (1992)

    MATH  Google Scholar 

  37. Witteveen, C., Roos, N., van der Krogt, R., de Weerdt, M.: Diagnosis of single and multi-agent plans. In: AAMAS 2005, pp. 805–812 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands

    Nico Roos

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  1. Nico Roos
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Correspondence to Nico Roos .

Editor information

Editors and Affiliations

  1. Artificial Intelligence, University of Groningen, Groningen, The Netherlands

    Bart Verheij

  2. Artificial Intelligence, University of Groningen, Groningen, The Netherlands

    Marco Wiering

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Roos, N. (2018). Learning-Based Diagnosis and Repair. In: Verheij, B., Wiering, M. (eds) Artificial Intelligence. BNAIC 2017. Communications in Computer and Information Science, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-76892-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-76892-2_1

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  • Online ISBN: 978-3-319-76892-2

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