Agent-Based Hybrid Intelligent Systems: An Agent-Based by Zili Zhang, Chengqi Zhang

By Zili Zhang, Chengqi Zhang

Solving complicated difficulties in real-world contexts, reminiscent of monetary funding making plans or mining huge information collections, contains many various sub-tasks, every one of which calls for assorted ideas. to house such difficulties, an exceptional variety of clever recommendations can be found, together with conventional innovations like specialist platforms ways and delicate computing ideas like fuzzy good judgment, neural networks, or genetic algorithms. those recommendations are complementary ways to clever info processing instead of competing ones, and therefore higher ends up in challenge fixing are completed whilst those recommendations are mixed in hybrid clever platforms. Multi-Agent platforms are splendid to version the manifold interactions among the diverse parts of hybrid clever systems.

This publication introduces agent-based hybrid clever structures and provides a framework and technique making an allowance for the improvement of such structures for real-world functions. The authors specialize in functions in monetary funding making plans and information mining.

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Extra info for Agent-Based Hybrid Intelligent Systems: An Agent-Based Framework for Complex Problem Solving

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PROBLEM ANALYSIS Redesign Identify sub−tasks Identify properties PROPERTY MATCHING Redesign what techniques can best solve the sub−tasks? HYBRID CATEGORY SELECTION Select hybrid classes Refinements IMPLEMENTATION Find best method of implementation VALIDATION Are the results correct? MAINTENANCE System monitoring Knowledge maintenance Retraining Fig. 5. Hybrid Intelligent System Development Cycle 28 2 Basics of Hybrid Intelligent Systems There are some shortcomings of the hybrid intelligent systems involved in following this development process.

Rescale fitness of population. • Determine maximum fitness of individuals in population. If |max f itness− optimum f itness| < tolerance then STOP. Otherwise, go to the second step. 2 Advantages and Disadvantages of Typical Intelligent Techniques There is an array of intelligent techniques, which can be divided into two main categories – traditional hard computing techniques and soft computing techniques. One typical hard computing technique is expert systems, while the principal members of soft computing techniques are fuzzy logic, neural networks, and genetic algorithms.

Once again, this is due to the independence of the intelligent system components. Finally, the verification and validation process is more difficult particularly for embedded applications. Fully-Integrated Models Fully-integrated systems share data structures and knowledge representations. Communication between the different components is accomplished via the dual nature (for example, symbolic and neural) of the structures. Reasoning is accomplished either cooperatively or through a component designated as the controller.

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