Intelligent agents in multimedia communication environments: Towards the design of adaptive services
Identifieur interne : 002B88 ( Hal/Corpus ); précédent : 002B87; suivant : 002B89Intelligent agents in multimedia communication environments: Towards the design of adaptive services
Auteurs : Romaric ChartonSource :
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Abstract
This thesis describes our works on interaction policies for heterogeneous multiagent systems (hMAS), where human beings are placed in the system, and their application for providing multimedia communication services adaptation. To deal with agent heterogeneity, our solution proposes to insert a mediator in the system. We have defined a model where agents assume generic roles that can be assembled to build service classes. A service arises from a collaboration that satisfies a subset of agents. In order to define the behavior of the agents, we have considered different kinds of planning and chosen to use reinforcement learning in Markov Decision Processes (MDP). We have also studied user modeling and interaction monitoring for the adaptation to other agents and to execution hazards. The thesis details the specification of a mediator that is able to help a user in an information retrieval service. It also presents our architecture with four abstraction levels and a software library that we have implemented to build and execute adaptive services.
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<front><div type="abstract" xml:lang="en">This thesis describes our works on interaction policies for heterogeneous multiagent systems (hMAS), where human beings are placed in the system, and their application for providing multimedia communication services adaptation. To deal with agent heterogeneity, our solution proposes to insert a mediator in the system. We have defined a model where agents assume generic roles that can be assembled to build service classes. A service arises from a collaboration that satisfies a subset of agents. In order to define the behavior of the agents, we have considered different kinds of planning and chosen to use reinforcement learning in Markov Decision Processes (MDP). We have also studied user modeling and interaction monitoring for the adaptation to other agents and to execution hazards. The thesis details the specification of a mediator that is able to help a user in an information retrieval service. It also presents our architecture with four abstraction levels and a software library that we have implemented to build and execute adaptive services.</div>
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<abstract xml:lang="en">This thesis describes our works on interaction policies for heterogeneous multiagent systems (hMAS), where human beings are placed in the system, and their application for providing multimedia communication services adaptation. To deal with agent heterogeneity, our solution proposes to insert a mediator in the system. We have defined a model where agents assume generic roles that can be assembled to build service classes. A service arises from a collaboration that satisfies a subset of agents. In order to define the behavior of the agents, we have considered different kinds of planning and chosen to use reinforcement learning in Markov Decision Processes (MDP). We have also studied user modeling and interaction monitoring for the adaptation to other agents and to execution hazards. The thesis details the specification of a mediator that is able to help a user in an information retrieval service. It also presents our architecture with four abstraction levels and a software library that we have implemented to build and execute adaptive services.</abstract>
<abstract xml:lang="fr">Cette thèse présente nos travaux sur les stratégies d'interaction dans les systèmes multi-agents hétérogènes (hSMA), où l'humain est placé dans le système, et leur application pour fournir des services de communication multimédia. Pour pallier l'hétérogénéité des agents, notre solution consiste à introduire un médiateur et à définir une modélisation où les agents endossent des rôles permettant de construire des classes de service. Un service résulte alors d'une collaboration donnant satisfaction à un sous-ensemble d'agents. Pour définir le comportement des agents, nous avons envisagé divers types de planification, puis l'apprentissage par renforcement dans les Processus de Décision Markoviens (MDP). Nous avons également étudié la modélisation des utilisateurs et le suivi de l'interaction pour l'adaptation aux autres agents et aux aléas d'exécution. La thèse décrit la spécification d'un médiateur capable d'aider un utilisateur dans sa recherche d'informations. Elle présente également une architecture à quatre niveaux d'abstraction et une bibliothèque logicielle utilisés pour concevoir et exécuter des services adaptatifs.</abstract>
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