By Zili Zhang, Chengqi Zhang
Solving advanced difficulties in real-world contexts, reminiscent of monetary funding making plans or mining huge facts collections, consists of many various sub-tasks, every one of which calls for diversified thoughts. to accommodate such difficulties, an outstanding range of clever strategies can be found, together with conventional recommendations like professional structures ways and tender computing recommendations like fuzzy good judgment, neural networks, or genetic algorithms. those options are complementary methods to clever info processing instead of competing ones, and hence higher leads to challenge fixing are completed while those recommendations are mixed in hybrid clever structures. Multi-Agent platforms are ultimate to version the manifold interactions one of many various elements of hybrid clever systems.
This ebook introduces agent-based hybrid clever platforms and offers a framework and method bearing in mind the advance of such structures for real-world purposes. The authors specialise in functions in monetary funding making plans and knowledge mining.
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Extra resources for Agent-Based Hybrid Intelligent Systems: An Agent-Based Framework for Complex Problem Solving
Compared to the more integrated intelligent system applications, loosely-coupled models are easy to develop. They are amenable to the use of commercially available intelligent systems software, which reduces the programming burden on developers. 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.
This improves the interactive capabilities of tightlycoupled models, in addition to enhancing their performance. Tight coupling has the beneﬁts of reduced communication overheads and improved runtime performance, compared to loose coupling. Several commercial packages are suitable for developing tightly-coupled models, and maintaining the modularity of the expert system and neural network components. Overall, tight coupling oﬀers design ﬂexibility and robust integration. Tightly-coupled systems have three principal limitations.
6 Summary Among the above ﬁve agent-based hybrid systems, the MIX, PREDICTOR, and architecture for fuzzy modeling only integrated very limited soft computing techniques. Both the MIX and PREDICTOR systems are focused on the integration of neural networks and symbolic technologies such as expert systems. The multi-agent architecture of Delgado et al. concentrated on the integration of diﬀerent fuzzy modeling techniques such as fuzzy clustering, fuzzy rule generation, and fuzzy rule tuning techniques.