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Authors: Chris Argenta and Jon Doyle

Affiliation: North Carolina State University, United States

ISBN: 978-989-758-220-2

Keyword(s): Multi-Agent Systems, Plan Recognition.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Formal Methods ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Methodologies and Methods ; Multi-Agent Systems ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Planning and Scheduling ; Sensor Networks ; Simulation and Modeling ; Soft Computing ; Software Engineering ; Symbolic Systems

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. (More)

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Paper citation in several formats:
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

@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},
}

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

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