Applications of DEC-MDPs in multi-robot systems

Aurélie Beynier 1 Abdel-Illah Mouaddib 2
1 SMA - Systèmes Multi-Agents
LIP6 - Laboratoire d'Informatique de Paris 6
2 Equipe MAD - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Optimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision problems are encountered while developing exploration rovers, teams of patrolling robots, rescue-robot colonies, mine-clearance robots, et cetera. In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We rst describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decen- tralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs.
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Aurélie Beynier, Abdel-Illah Mouaddib. Applications of DEC-MDPs in multi-robot systems. Enrique Sucar, Eduardo Morales, Jesse Hoey. Decision Theory Models for Applications in Artificial Intelligence Concepts and Solutions, IGI Global, pp.361-384, 2011, 978-1609601652. ⟨10.4018/978-1-60960-165-2.ch016⟩. ⟨hal-01344447⟩

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