By Zhang Ch.
Fixing complicated difficulties in real-world contexts, corresponding to monetary funding making plans or mining huge information collections, consists of many various sub-tasks, each one of which calls for varied innovations. to accommodate such difficulties, a very good range of clever thoughts can be found, together with conventional suggestions like specialist platforms techniques and delicate computing ideas like fuzzy good judgment, neural networks, or genetic algorithms. those strategies are complementary methods to clever info processing instead of competing ones, and therefore higher leads to challenge fixing are completed whilst those strategies are mixed in hybrid clever structures. Multi-Agent structures are very best to version the manifold interactions among the varied elements of hybrid clever systems.This ebook introduces agent-based hybrid clever structures and offers a framework and technique making an allowance for the improvement of such structures for real-world purposes. The authors concentrate on purposes in monetary funding making plans and information mining.
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Extra resources for Agent-Based Hybrid Intelligent Systems: An Agent-Based Fromework for Complex Problem Solving
Transformational models offer several benefits to developers. They are often quick to develop, and ultimately require maintenance on only one system. Development occurs in the most appropriate environment. Similarly, the delivery technique offers operational benefits suitable to its environment. Limitations to transformational models are significant. First, a fully automated means of transforming an expert system to a neural network, or vice versa, is still needed. Also, significant modifications to the system may require a new development effort, which leads to another transformation.
A fundamental stimulus to investigations into hybrid intelligent systems is the awareness in the research and development communities that combined approaches will be necessary if the remaining tough problems in artificial intelligence are to be solved. The successes in integrating expert systems and neural networks, and the advances in theoretical research on hybrid systems, point to similar opportunities for when other intelligent technologies are included in the mix. From a knowledge of their strengths and weaknesses, we can construct hybrid systems to mitigate the limitations and take advantage of the opportunities to produce systems that are more powerful than those that could be built with single technologies.
Both the system design and implementation processes are simplified with loosely-coupled models. Finally, maintenance time is reduced because of the simplicity of the data file interface mechanism. Some limitations are associated with loosely-coupled models. Because of the file-transfer interface, communication costs are high and operating time is longer. The development of separate intelligent system components leads to redundancy of effort. Both must be capable of solving subproblems in order to perform their unique computations.