By Zili Zhang, Chengqi Zhang
Solving advanced difficulties in real-world contexts, resembling monetary funding making plans or mining huge information collections, includes many various sub-tasks, every one of which calls for assorted suggestions. to accommodate such difficulties, a good range of clever strategies can be found, together with conventional thoughts like specialist structures methods and smooth computing thoughts like fuzzy common sense, neural networks, or genetic algorithms. those options are complementary methods to clever info processing instead of competing ones, and hence larger leads to challenge fixing are accomplished whilst those thoughts are mixed in hybrid clever structures. Multi-Agent platforms are ideal to version the manifold interactions one of several varied parts of hybrid clever systems.
This ebook introduces agent-based hybrid clever structures and offers a framework and technique bearing in mind the advance of such platforms for real-world functions. The authors specialize in functions in monetary funding making plans and knowledge mining.
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Additional resources for Agent-Based Hybrid Intelligent Systems: An Agent-Based Framework for Complex Problem Solving
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. But, because they lack direct access to each other’s internal processing, they must develop independent capabilities. This may also lead to overlap in data input environments and internal processing. Tightly-Coupled Models The categories of loose and tight coupling have signiﬁcant overlap. Both utilize independent expert system and neural network components.
For example, the MASE (Multi-Agent Systems Engineering) methodology [89, 86, 88] provides clean guidelines for developing multi-agent systems, based on a well-deﬁned seven-step process (capturing goals, applying use cases, reﬁning roles, creating agent classes, constructing conversations, assembling agent classes, and system design). This process drives developers from analysis to implementation. However, once again, the design process fails to identify any organizational abstraction other than the role model.
For example, we can build some kind of decision making about whether to execute a method into the method itself, and in this way achieve a stronger kind of autonomy for our objects. However, the point is that autonomy of this kind is not a component of the basic object-oriented model. 32 3 Basics of Agents and Multi-agent Systems The second important distinction between object and agent systems is with respect to the notion of ﬂexible (reactive, pro-active, social) autonomous behaviour. The standard object model has nothing whatsoever to say about how to build systems that integrate these types of behaviour.