Multi-Agent Plan Recognition as Planning (MAPRAP)

Chris Argenta, Jon Doyle

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.

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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

@conference{icaart16,
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,},
year={2016},
pages={141-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005707701410148},
isbn={978-989-758-172-4},
}


in EndNote Style

TY - CONF
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