By Zhang Ch.
Fixing advanced difficulties in real-world contexts, reminiscent of monetary funding making plans or mining huge info collections, consists of many alternative sub-tasks, each one of which calls for diverse concepts. to house such difficulties, a good variety of clever strategies can be found, together with conventional suggestions like specialist structures techniques and tender computing strategies like fuzzy good judgment, neural networks, or genetic algorithms. those innovations are complementary techniques to clever info processing instead of competing ones, and therefore larger ends up in challenge fixing are completed whilst those ideas are mixed in hybrid clever structures. Multi-Agent structures are ideal to version the manifold interactions one of the diverse parts of hybrid clever systems.This booklet introduces agent-based hybrid clever platforms and offers a framework and method making an allowance for the improvement of such platforms for real-world purposes. The authors concentrate on purposes in monetary funding making plans and information mining.
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Additional info for Agent-Based Hybrid Intelligent Systems: An Agent-Based Fromework for Complex Problem Solving
Many hybrid intelligent systems used in different application fields appeared in the past ten years [39, 65, 66, 67, 68, 69]. All these systems fall into the three classes in Goonatilake’s scheme. A typical development cycle in the implementation of these hybrid intelligent systems is shown in Fig. 5. This is based on object-oriented techniques. There are six stages in the construction of hybrid intelligent systems: problem analysis, property matching, hybrid category selection, implementation, validation, and maintenance .
3 (adapted from ) shows the classic three-layer feedforward neural network architecture. 3 actually represents a function acting on a vector, or list of numbers. The notation is used to denote the space of all vectors of the form where each is a real number and is an integer. for example, is a line, and is a two-dimensional plane. The neural network function sends the vector in to the vector Thus, the feedforward network can be represented as: where and The action of this function is determined in a specific way.
1 (adapted from ). The major parts of the system are the knowledge base and the inference engine. The knowledge base consists of facts and rules about the subject at hand. The inference engine consists of all processes that manipulate the knowledge base to deduce information requested by the user – resolution or forward or backward chaining, for example. The user interface might consist of some kind of natural language processing system that allows the user to interact with the system in a limited form of natural language.
Agent-Based Hybrid Intelligent Systems: An Agent-Based Fromework for Complex Problem Solving by Zhang Ch.