Multi-Agent Plan Recognition as Planning (MAPRAP)

Chris Argenta, Jon Doyle


A key challenge in Multi-agent Plan Recognition (MPAR) is effectively pruning the large search space of potential goal / team compositions because multi-agent scenarios distribute actions/observables across agents. This additional dimension also makes creating a priori plan libraries difficult. In this paper, we describe our strategy for discrete Multi-agent Plan Recognition as Planning (MAPRAP), which extends Ramirez and Geffner’s Plan Recognition as Planning (PRAP) approach into multi-agent domains. MAPRAP (like PRAP) uses a planning domain (not a library) to synthesize and compare utility costs of plan instances that incorporate potential goals and previous observables to identify the plan being carried out by teams of agents. This initial discrete implementation of MAPRAP includes two pruning strategies to address the explosion of hypotheses. We establish a performance profile for discrete MAPRAP using the well-known multi-agent blocks-world benchmark domain. We varied the number of teams, agent count, and goal sizes. We measured accuracy, precision, and recall at each time step. For pruning efficiency, we compare two strategies. In the more aggressive case our multi-agent team blocks scenarios averaged 1.05 plans synthesized per goal per time step (compared to 0.56 for single agent scenarios) demonstrating feasibility of MAPRAP and benchmarking for future improvements.


  1. Banerjee B, Kraemer L, and Lyle J (2010) “Multi-Agent Plan Recognition: Formalization and Algorithms,” AAAI 2010.
  2. Banerjee B, Lyle J, and Kraemer L (2011) “New Algorithms and Hardness Results for Multi-Agent Plan Recognition,” AAAI 2011.
  3. 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.
  4. Genersereth M and Love N (2005) “General Game Playing: Overview of the AAAI Competition,” AI Magazine, vol. 26, no. 2.
  5. Intille S S and Bobick A F (2001) “Recognizing planned, multi-person action,” Computer Vision and Image Understanding, vol. 81, pp. 414-445.
  6. Kovacs D (2012) “A Multi-Agent Extension of PDDL3.1,” WS-IPC 2012:19.
  7. McDermott D and AIPS-98 Planning Competition Committee (1998) “PDDL-the planning domain definition language”
  8. 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).
  9. Pellier D (2014) “PDDL4J and GraphPlan open source implementation,”
  10. Ramirez M and Geffner H, (2009) “Plan recognition as planning,” in Proceedings of the 21st international joint conference on Artificial intelligence.
  11. Ramirez M and Geffner H (2010) “Probabilistic Plan Recognition using off-the-shelf Classical Planners,” Proc. AAAI-10.
  12. Sadilek A and Kautz H (2010) “Recognizing Multi-Agent Activities from GPS Data,” in Twenty-Fourth AAAI Conference on Artificial Intelligence.
  13. Sukthankar G, Goldman R P, Geib C, Pynadath D V, Bui H H (2014) “Plan, Activity, and Intent Recognition Theory and Practice.” Morgan Kaufmann.
  14. 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).
  15. 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).
  16. web (2014) "Bell Numbers" Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc.
  17. Zhuo H H, Yang Q, and Kambhampati S (2012) "Actionmodel based multi-agent plan recognition." Advances in Neural Information Processing Systems 25.

Paper Citation

in Harvard Style

Argenta C. and Doyle J. (2016). Multi-Agent Plan Recognition as Planning (MAPRAP) . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 141-148. DOI: 10.5220/0005707701410148

in Bibtex Style

author={Chris Argenta and Jon Doyle},
title={Multi-Agent Plan Recognition as Planning (MAPRAP)},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Multi-Agent Plan Recognition as Planning (MAPRAP)
SN - 978-989-758-172-4
AU - Argenta C.
AU - Doyle J.
PY - 2016
SP - 141
EP - 148
DO - 10.5220/0005707701410148