Probabilistic Multi-Agent Plan Recognition as Planning (P-Maprap): Recognizing Teams, Goals, and Plans from Action Sequences

Chris Argenta, Jon Doyle

Abstract

We extend Multi-agent Plan Recognition as Planning (MAPRAP) to Probabilistic MAPRAP (P-MAPRAP), which probabilistically identifies teams and their goals from limited observations of on-going individual agent actions and a description of actions and their effects. These methods do not rely on plan libraries, as such are infeasibly large and complex in multi-agent domains. Both MAPRAP and P-MAPRAP synthesize plans tailored to hypothesized team compositions and previous observations. Where MAPRAP prunes team-goal interpretations on optimality grounds, P-MAPRAP directs its search base on a likelihood ranking of interpretations, thus effectively reducing the synthesis effort needed without compromising recognition. We evaluate performance in scenarios that vary the number of teams, agent counts, initial states, goals, and observation errors, assuming equal base-rates. We measure accuracy, precision, and recall online to evaluate early stage recognition. Our results suggest that compared to MAPRAP, P-MAPRAP exhibits improved speed and recognition accuracy.

References

  1. Argenta C, and Doyle J (2015) “Multi-Agent Plan Recognition as Planning (MAPRAP),” In Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016) - Volume 2, pages 141-148
  2. Banerjee B, Kraemer L, and Lyle J (2010) “Multi-Agent Plan Recognition: Formalization and Algorithms,” AAAI 2010
  3. Banerjee B, Lyle J, and Kraemer L (2011) “New Algorithms and Hardness Results for Multi-Agent Plan Recognition,” AAAI 2011
  4. Cohen P R, Perrault C R, and Allen J F (1981) “Beyond Question Answering,” in Strategies for Natural Language Processing, NJ: Hillsdale, pp. 245-274.
  5. Genersereth M and Love N (2005) “General Game Playing: Overview of the AAAI Competition,” AI Magazine, vol. 26, no. 2
  6. Intille S S and Bobick A F (2001) “Recognizing planned, multi-person action,” Computer Vision and Image Understanding, vol. 81, pp. 414-445
  7. Kovacs D (2012) “A Multi-Agent Extension of PDDL3.1,” WS-IPC 2012:19
  8. McDermott D and AIPS-98 Planning Competition Committee (1998) “PDDL-the planning domain definition language”
  9. Muise C, Lipovetzky N, Ramirez M (2014) “MAPLAPKT: Omnipotent Multi-Agent Planning via Compilation to Classical Planning,” Competition of Distributed and Multi-Agent Planners (CoDMAP-15)
  10. Pellier D (2014) “PDDL4J and GraphPlan open source implementation,” http://sourceforge.net/projects/pdd4j
  11. Ramirez M and Geffner H, (2009) “Plan recognition as planning,” in Proceedings of the 21st international joint conference on Artificial intelligence
  12. Ramirez M and Geffner H (2010) “Probabilistic Plan Recognition using off-the-shelf Classical Planners,” Proc. AAAI-10
  13. Sadilek A and Kautz H (2010) “Recognizing Multi-Agent Activities from GPS Data,” in Twenty-Fourth AAAI Conference on Artificial Intelligence
  14. Sukthankar G, Goldman R P, Geib C, Pynadath D V, Bui H H (2014) “Plan, Activity, and Intent Recognition Theory and Practice.” Morgan Kaufmann
  15. Sukthankar G and Sycara K (2006) “Simultaneous Team Assignment and Behavior Recognition from Spatiotemporal Agent Traces,” Proceedings of the TwentyFirst National Conference on Artificial Intelligence (AAAI-06)
  16. Sukthankar G and Sycara K (2008) “Efficient Plan Recognition for Dynamic Multi-agent Teams,” Proceedings of 7th International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2008)
  17. Zhuo H H, Yang Q, and Kambhampati S (2012) "Actionmodel based multi-agent plan recognition." Advances in Neural Information Processing Systems 25
Download


Paper Citation


in Harvard Style

Argenta C. and Doyle J. (2017). Probabilistic Multi-Agent Plan Recognition as Planning (P-Maprap): Recognizing Teams, Goals, and Plans from Action Sequences . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 575-582. DOI: 10.5220/0006197505750582


in Bibtex Style

@conference{icaart17,
author={Chris Argenta and Jon Doyle},
title={Probabilistic Multi-Agent Plan Recognition as Planning (P-Maprap): Recognizing Teams, Goals, and Plans from Action Sequences},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={575-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006197505750582},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Probabilistic Multi-Agent Plan Recognition as Planning (P-Maprap): Recognizing Teams, Goals, and Plans from Action Sequences
SN - 978-989-758-220-2
AU - Argenta C.
AU - Doyle J.
PY - 2017
SP - 575
EP - 582
DO - 10.5220/0006197505750582