Agent intelligence through data mining by Andreas L. Symeonidis

By Andreas L. Symeonidis

Wisdom, hidden in voluminous facts repositories generally created and maintained by means of todays purposes, may be extracted through info mining. the next move is to rework this came upon wisdom into the inference mechanisms or just the habit of brokers and multi-agent structures. Agent Intelligence via facts Mining addresses this factor, in addition to the controversial problem of producing intelligence from information whereas moving it to a separate, most likely self reliant, software program entity. This publication features a method, instruments and strategies, and a number of other examples of agent-based functions constructed with this strategy. This quantity focuses usually at the use of information mining for smarter, extra effective brokers.

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Let there be N d-dimensional data tuples or data objects O{ in a subcluster. CF summarizes information about subclusters of objects and is defined as a triplet (iV, LS, SS), where TV is the number of data objects residing within the cluster, LS is the sum of the data objects (J2i=i °i) a n d SS is their square sum (52,i=i °f)• The CF-tree is a weighted tree with two parameters: the branching factor B and threshold T. B determines the maximum number of children a tree node may have, while T determines the maximum diameter of a subcluster residing in the leaves.

Thus, one of the most characteristic agent features is its agency. In the rest of this book, the term agent is synonymous to a software agent. The difficulty in defining an agent arises from the fact that the various aspects of agency are weighted differently, with respect to the application domain at hand. Although, for example, agent learning is considered of pivotal importance in certain applications, for others it may be considered not only trivial, but even undesirable. Consequently, a number of definitions could be provided with respect to agent objectives.

Economics: Pricing policies, financial market simulations, and risk assessment have been performed with the use of GAs. Ecology: GAs have been employed for the modeling and simulation of ecological problems, like species survival, symbiosis etc. Population genetics: The study of populations and their evolution has been supported by the use of GAs. Evolution and learning: GAs have been used to improve the interaction between the members of a population. Social Modeling: Finally, GAs have been used for the study of societal issues and their impact on populations.

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