Authors:
Erik Hernández
;
Antonio Barrientos
;
Jaime del Cerro
and
Claudio Rossi
Affiliation:
Technical University of Madrid, Spain
Keyword(s):
Multi-robot System, Security Tasks, Patrolling problem, Stochastic Fictitious Play, Game Theory.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Collective Intelligence
;
Cooperation and Coordination
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Mobile Agents
;
Multi-Agent Systems
;
Robot and Multi-Robot Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
A great deal of work has been done in recent years on the multi-robot patrolling problem. In such problem a team of robots is engaged to supervise an infrastructure. Commonly, the patrolling tasks are performed with the objective of visiting a set of points of interest. This problem has been solved in the literature by developing deterministic and centralized solutions, which perform better than decentralized and non-deterministic approaches in almost all cases. However, deterministic methods are not suitable for security purpose due to their predictability. This work provides a new decentralized and non-deterministic approach based on the model of Game Theory called Stochastic Fictitious Play (SFP) to perform security tasks at critical facilities. Moreover, a detailed study aims at providing additional insight of this learning model into the multi-robot patrolling context is presented. Finally, the approach developed in this work is analyzed and compared with other methods proposed
in the literature by utilizing a patrolling simulator.
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