Skip to main content

Temporal Multiagent Plan Execution: Explaining What Happened

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11763))

Abstract

The paper addresses the problem of explaining failures that happened during the execution of Temporal Multiagent Plans (TMAPs), namely MAPs that contain both logic and temporal constraints about the action conditions and effects. We focus particularly on computing explanations that help the user figure out how failures in the execution of one or more actions propagated to later actions. To this end, we define a model that enriches knowledge about the nominal execution of the actions with knowledge about (faulty) execution modes. We present an algorithm for computing diagnoses of TMAPs execution failures, where each diagnosis identifies the actions that executed in a faulty mode, and those that failed instead because of the propagation of other failures. Diagnoses are then integrated with temporal explanations, that detail what happened during the plan execution by specifying temporal relations between the relevant events.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    For the sake of discussion, we assume that all modes M(ac) of an action ac have the same preconditions pre.

References

  1. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    Article  Google Scholar 

  2. Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K.: Explainable agents and robots: results from a systematic literature review. In: Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019, Montreal, QC, Canada, 13–17 May 2019, pp. 1078–1088 (2019)

    Google Scholar 

  3. de Moura, L., Bjørner, N.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78800-3_24

    Chapter  Google Scholar 

  4. Elimelech, O., Stern, R., Kalech, M., Bar-Zeev, Y.: Diagnosing resource usage failures in multi-agent systems. Expert Syst. Appl. 77, 44–56 (2017)

    Article  Google Scholar 

  5. Fox, M., Long, D.: PDDL2.1: an extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. (JAIR) 20, 61–124 (2003)

    Article  Google Scholar 

  6. Goldszmidt, M., Pearl, J.: Rank-based systems: a simple approach to belief revision, belief update, and reasoning about evidence and actions. In: Proceeding of the KR 1992, pp. 661–672 (1992)

    Google Scholar 

  7. Grastien, A.: Diagnosis of hybrid systems with SMT: opportunities and challenges. In: Proceedings of the 21st European Conference on AI, pp. 405–410. IOS Press (2014)

    Google Scholar 

  8. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 93 (2018)

    Article  Google Scholar 

  9. de Jonge, F., Roos, N., Witteveen, C.: Primary and secondary diagnosis of multi-agent plan execution. Auton. Agents Multi-Agent Syst. 18, 267–294 (2009)

    Article  Google Scholar 

  10. Kalech, M., Kaminka, G.A.: On the design of coordination diagnosis algorithms for teams of situated agents. Artif. Intell. 171(8–9), 491–513 (2007)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  12. Micalizio, R., Torta, G.: Diagnosing delays in multi-agent plans execution. In: Proceedings of the 20th ECAI, pp. 594–599. IOS Press (2012)

    Google Scholar 

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

    Article  Google Scholar 

  14. Policella, N., Smith, S.F., Cesta, A., Oddi, A.: Generating robust schedules through temporal flexibility. In: ICAPS, vol. 4, pp. 209–218 (2004)

    Google Scholar 

  15. Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)

    Article  MathSciNet  Google Scholar 

  16. Roos, N., Witteveen, C.: Diagnosis of simple temporal networks. In: Proceedings of ECAI 2008, pp. 593–597 (2008)

    Google Scholar 

  17. Torta, G., Micalizio, R., Sormano, S.: Explaining failures propagations in the execution of multi-agent temporal plans. In: Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019, Montreal, QC, Canada, 13–17 May 2019, pp. 2232–2234 (2019)

    Google Scholar 

  18. Williams, B.C., Ragno, R.J.: Conflict-directed A* and its role in model-based embedded systems. Discrete Appl. Math. 155(12), 1562–1595 (2007)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gianluca Torta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Torta, G., Micalizio, R., Sormano, S. (2019). Temporal Multiagent Plan Execution: Explaining What Happened. In: Calvaresi, D., Najjar, A., Schumacher, M., Främling, K. (eds) Explainable, Transparent Autonomous Agents and Multi-Agent Systems. EXTRAAMAS 2019. Lecture Notes in Computer Science(), vol 11763. Springer, Cham. https://doi.org/10.1007/978-3-030-30391-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30391-4_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30390-7

  • Online ISBN: 978-3-030-30391-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics