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Case-Based Policy and Goal Recognition

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Case-Based Reasoning Research and Development (ICCBR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9343))

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Abstract

We present the Policy and Goal Recognizer (PaGR), a case-based system for multiagent keyhole recognition. PaGR is a knowledge recognition component within a decision-making agent that controls simulated unmanned air vehicles in Beyond Visual Range combat. PaGR stores in a case the goal, observations, and policy of a hostile aircraft, and uses cases to recognize the policies and goals of newly-observed hostile aircraft. In our empirical study of PaGR’s performance, we report evidence that knowledge of an adversary’s goal improves policy recognition. We also show that PaGR can recognize when its assumptions about the hostile agent’s goal are incorrect, and can often correct these assumptions. We show that this ability improves PaGR’s policy recognition performance in comparison to a baseline algorithm.

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References

  1. Carberry, S.: Techniques for plan recognition. User Model. User-Adap. Inter. 11(1–2), 31–48 (2001)

    Article  MATH  Google Scholar 

  2. Borck, H., Karneeb, J., Alford, R., Aha, D.W.: Case-based behavior recognition in beyond visual range air combat. In: Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference. AAAI Press (2015)

    Google Scholar 

  3. Muñoz-Avila, H., Jaidee, U., Aha, D.W., Carter, E.: Goal-driven autonomy with case-based reasoning. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 228–241. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  4. Molineaux, M., Klenk, M., Aha, D.W.: Goal-driven autonomy in a navy strategy simulation. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press (2010)

    Google Scholar 

  5. Borck, H., Karneeb, J., Alford, R., Aha, D.W.: Case-based behavior recognition to facilitate planning in unmanned air vehicles. In: Vattam, S.S., Aha, D.W., eds.: Case-Based Agents: Papers from the ICCBR Workshop, Technical report. University College Cork, Cork, Ireland (2014)

    Google Scholar 

  6. Sukthankar, I.G., Goldman, R., Geib, C., Pynadath, D., Bui, H.: An introduction to plan, activity, and intent recognition. In: Sukthankar, I.G., Goldman, R., Geib, C., Pynadath, D., Bui, H. (eds.) Plan, Activity, and Intent Recognition. Elsevier (2014)

    Google Scholar 

  7. Vattam, S.S., Aha, D.W., Floyd, M.: Case-based plan recognition using action sequence graphs. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS, vol. 8765, pp. 495–510. Springer, Heidelberg (2014)

    Google Scholar 

  8. Ontañón, S., Lee, Y.-C., Snodgrass, S., Bonfiglio, D., Winston, F.K., McDonald, C., Gonzalez, A.J.: Case-based prediction of teen driver behavior and skill. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS, vol. 8765, pp. 375–389. Springer, Heidelberg (2014)

    Google Scholar 

  9. Fagundes, M.S., Meneguzzi, F., Bordini, R.H., Vieira, R.: Dealing with ambiguity in plan recognition under time constraints. In: Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, pp. 389–396. ACM Press (2014)

    Google Scholar 

  10. Alford, R., Borck, H., Karneeb, J., Aha, D.W.: Active behavior recognition in beyond visual range combat. In: Proceedings of the Third Conference on Advances in Cognitive Systems, Cognitive Systems Foundation (2015)

    Google Scholar 

  11. Laviers, K., Sukthankar, G.: A real-time opponent modeling system for Rush Football. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 2476–2481. AAAI Press (2011)

    Google Scholar 

  12. Molineaux, M., Aha, D.W., Sukthankar, G.: Beating the defense: using plan recognition to inform learning agents. In: Proceedings of the Twenty-Second International Florida Artificial Intelligence Research Society Conference, pp. 337–343. AAAI Press (2009)

    Google Scholar 

  13. Levine, S.J., Williams, B.C.: Concurrent plan recognition and execution for human-robot teams. In: Twenty-Fourth International Conference on Automated Planning and Scheduling. ACM Press (2014)

    Google Scholar 

  14. Banerjee, B., Lyle, J., Kraemer, L.: The complexity of multi-agent plan recognition. Auton. Agent. Multi-Agent Syst. 29(1), 40–72 (2015)

    Article  Google Scholar 

  15. Zhuo, H.H., Li, L.: Multi-agent plan recognition with partial team traces and plan libraries. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 484–489. AAAI Press (2011)

    Google Scholar 

  16. Geib, C.W., Goldman, R.P.: Plan recognition in intrusion detection systems. In: Proceedings of the DARPA Information Survivability Conference, pp. 46–55. IEEE Press (2001)

    Google Scholar 

  17. Corchado, J.M., Pavón, J., Corchado, E., Castillo, L.F.: Development of CBR-BDI agents: a tourist guide application. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 547–559. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  18. Ontañón, S., Mishra, K., Sugandh, N., Ram, A.: Case-based planning and execution for real-time strategy games. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 164–178. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  19. Rubin, J., Watson, I.: On combining decisions from multiple expert imitators for performance. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 344–349. AAAI Press (2011)

    Google Scholar 

  20. Floyd, M.W., Esfandiari, B., Lam, K.: A case-based reasoning approach to imitating RoboCup players. In: Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference, pp. 251–256. AAAI Press (2008)

    Google Scholar 

  21. Zarka, R., Cordier, A., Egyed-Zsigmond, E., Lamontagne, L., Mille, A.: Similarity measures to compare episodes in modeled traces. In: Delany, S.J., Ontañón, S. (eds.) ICCBR 2013. LNCS, vol. 7969, pp. 358–372. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  22. Sánchez-Marré, M., Cortés, U., Martínez, M., Comas, J., Rodríguez-Roda, I.: An approach for temporal case-based reasoning: episode-based reasoning. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 465–476. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  23. Borrajo, D., Roubíčková, A., Serina, I.: Progress in case-based planning. ACM Comput. Surv. 47(2), 1–39 (2015)

    Article  Google Scholar 

  24. Jensen, B., Karneeb, J., Borck, H., Aha, D.: Integrating AFSIM as an internal predictor. Technical report AIC-14-172, Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence, Washington, DC (2014)

    Google Scholar 

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Acknowledgements

Thanks to OSD ASD (R&E) for supporting this research. Thanks also to our subject matter experts for their many contributions and to the reviewers for their helpful comments.

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Authors and Affiliations

  1. Knexus Research Corporation, Springfield, VA, USA

    Hayley Borck, Justin Karneeb & Michael W. Floyd

  2. ASEE Postdoctoral Fellow, Washington, DC, USA

    Ron Alford

  3. Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory (Code 5514), Washington, DC, USA

    Ron Alford & David W. Aha

Authors
  1. Hayley Borck
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  2. Justin Karneeb
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  3. Michael W. Floyd
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  4. Ron Alford
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  5. David W. Aha
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Corresponding author

Correspondence to Hayley Borck .

Editor information

Editors and Affiliations

  1. Universität Paderborn, Paderborn, Germany

    Eyke Hüllermeier

  2. Goethe-Universität Frankfurt, Frankfurt/Main, Germany

    Mirjam Minor

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Borck, H., Karneeb, J., Floyd, M.W., Alford, R., Aha, D.W. (2015). Case-Based Policy and Goal Recognition. In: Hüllermeier, E., Minor, M. (eds) Case-Based Reasoning Research and Development. ICCBR 2015. Lecture Notes in Computer Science(), vol 9343. Springer, Cham. https://doi.org/10.1007/978-3-319-24586-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-24586-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24585-0

  • Online ISBN: 978-3-319-24586-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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