Computer Networks and ISDN Systems
Internet economics
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A POMDP formulation of preference elicitation problems
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A User-Guided Cognitive Agent for Network Service Selection in Pervasive Computing Environments
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Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
A context-aware mobile service discovery and selection mechanism using artificial neural networks
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Computer Networks: The International Journal of Computer and Telecommunications Networking
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
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CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
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Personal and Ubiquitous Computing
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The problem of interest is how to dynamically allocate wireless access services in a competitive market which implements a take-it-or-leave-it allocation mechanism. In this paper we focus on the subproblem of preference elicitation, given a mechanism. The user, due to a number of cognitive and technical reasons, is assumed to be initially uninformed over their preferences in the wireless domain. The solution we have developed is a closed-loop user-agent system that assists the user in application, task and context dependent service provisioning by adaptively and interactively learning to select the best wireless data service. The agent learns an incrementally revealed user preference model given explicit or implicit feedback on its decisions by the user. We model this closed-loop system as a Markov Decision Process, where the agent actions are rewarded by the user, and show how a reinforcement learning algorithm can be used to learn a model of the userýs preferences on-line in the given allocation mechanism. We evaluate the performance and value of the agent in a series of preliminary empirical user studies.