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Application Of Fuzzy State Aggregation And Policy Hill Climbing To Multi-Agent Systems In Stochastic Environments, Dean C. Wardell
Application Of Fuzzy State Aggregation And Policy Hill Climbing To Multi-Agent Systems In Stochastic Environments, Dean C. Wardell
Theses and Dissertations
Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually even as the operating environment changes. Applying this learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the policy hill climbing methods of Win …
A Comparison Of Methods To Construct An Optimal Membership Function In A Fuzzy Database System, Joanne Marie Cunningham
A Comparison Of Methods To Construct An Optimal Membership Function In A Fuzzy Database System, Joanne Marie Cunningham
Theses and Dissertations
A fuzzy set is one in which membership in a category is not Boolean, rather items have a degree of membership. Fuzzy databases expand on this idea by storing fuzzy data and allowing data to be retrieved based on its degree of membership. Determining the degree of membership that satisfies the largest number of users is difficult. Five different methods of determining the membership function: the Direct Rating Method, the Random Method with step sizes of .02 and .03, the Steplock Method, and the Weighted Average Method, were compared on the basis of convergence and user satisfaction. The results support …