By Zili Zhang, Chengqi Zhang
Solving complicated difficulties in real-world contexts, similar to monetary funding making plans or mining huge info collections, includes many alternative sub-tasks, every one of which calls for diverse recommendations. to accommodate such difficulties, an excellent variety of clever options can be found, together with conventional suggestions like specialist platforms techniques and tender computing innovations like fuzzy common sense, neural networks, or genetic algorithms. those options are complementary methods to clever info processing instead of competing ones, and therefore greater ends up in challenge fixing are completed while those innovations are mixed in hybrid clever structures. Multi-Agent platforms are perfect to version the manifold interactions one of several diversified elements of hybrid clever systems.
This e-book introduces agent-based hybrid clever structures and offers a framework and technique taking into account the advance of such structures for real-world functions. The authors specialise in functions in monetary funding making plans and knowledge mining.
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Additional info for Agent-Based Hybrid Intelligent Systems: An Agent-Based Framework for Complex Problem Solving
1 Outline of the Methodology In any agent-based hybrid intelligent system for real-world applications, the dynamic arrival of unknown agents needs to be taken into account, but with no self-interested behavior in the course of the interactions. The Gaia methodology is ill suited to handling the dynamic arrival of new agents into the system, whereas coordination-oriented methodology is focused on the processing of self-interested agents. Thus both methodologies cannot be applied to the analysis and design of hybrid intelligent systems directly.
These include information retrieval, user interface design, robotics, electronic commerce, computer mediated collaboration, computer games, education and training, smart environments, ubiquitous computers, and social simulation. This is not only a very promising technology, it is emerging as a new way of thinking, a conceptual paradigm for analyzing problems and for designing systems, for dealing with complexity, distribution and interactivity, and perhaps a new perspective on computing and intelligence.
Another diﬃculty lies in the hybrid category selection phase. At this stage, developers must choose the type of hybrid system required (function-replacing, inter-communicating, or polymorphic) for solving the speciﬁc problem. This is not easy. The inherent complexity of the hybrid intelligent systems means it is impossible to know a priori about all potential links or relationships among components that comprise a system. Interactions will occur at unpredictable times, for unpredictable reasons, and between unpredictable components.