Modeling the User through Reinforcement Learning: A Pre-Thesis Proposal
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منابع مشابه
Web pages ranking algorithm based on reinforcement learning and user feedback
The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. In this paper, a ranking algorithm based on the reinforcement le...
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Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the rank...
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Current approaches to user modeling largely are specific to an application, do not generalize easily and have no theoretical foundation. Viewing the user as an agent acting in an environment and assuming that the agent can be modeled in term of standard methods from Artificial Intelligence is one way to address these issues. Here, the user is modeled as solving a Markov Decision Problem. This a...
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We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our framework can be applied to the power management problem in both infrastructure and ad hoc wireless networks. From this thesis we conclude that mid-level power management policies can...
متن کاملThesis Summary: Nonparametric Bayesian Approaches for Reinforcement Learning in Partially Observable Domains
The objective of my doctoral research is bring together two fields: partially-observable reinforcement learning (PORL) and non-parametric Bayesian statistics (NPB) to address issues of statistical modeling and decisionmaking in complex, realworld domains.
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تاریخ انتشار 2002