Intelligent agents in multimedia communication environments: Towards the design of adaptive services
Identifieur interne : 007537 ( Main/Merge ); précédent : 007536; suivant : 007538Intelligent agents in multimedia communication environments: Towards the design of adaptive services
Auteurs : Romaric Charton [France]Source :
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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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