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Authors: Chris Argenta and Jon Doyle

Affiliation: North Carolina State University, United States

ISBN: 978-989-758-172-4

ISSN: 2184-433X

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 ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Methodologies and Methods ; Multi-Agent Systems ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Software Engineering ; Symbolic Systems

Abstract: 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. (More)

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Paper citation in several formats:
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, ISSN 2184-433X, pages 141-148. DOI: 10.5220/0005707701410148

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,},


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

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