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

Chris Argenta, Jon Doyle

2017

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.

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