By Zhang Ch.
Fixing complicated difficulties in real-world contexts, resembling monetary funding making plans or mining huge facts collections, contains many various sub-tasks, every one of which calls for various innovations. to accommodate such difficulties, an exceptional range of clever concepts can be found, together with conventional recommendations like professional structures ways and gentle computing ideas like fuzzy common sense, neural networks, or genetic algorithms. those thoughts are complementary ways to clever details processing instead of competing ones, and hence larger leads to challenge fixing are completed while those suggestions are mixed in hybrid clever structures. Multi-Agent structures are supreme to version the manifold interactions among the various elements of hybrid clever systems.This ebook introduces agent-based hybrid clever platforms and provides a framework and technique taking into account the improvement 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 Fromework for Complex Problem Solving
While there are obvious similarities, there are also significant differences between agents and objects. The first is in the degree to which agents and objects are autonomous. Recall that the defining characteristic of object-oriented programming is the principle of encapsulation–the idea that objects can have control over their own internal state. In programming languages like Java, we can declare instance variables (and methods) to be private, meaning they are only accessible from within the object.
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. However, tightlycoupled systems pass information via memory resident data structures rather than external data files. This improves the interactive capabilities of tightlycoupled models, in addition to enhancing their performance.
Both must be updated simultaneously to avoid confusion, and updates to one cannot help the other. Fig. 4. Models for Integrating Intelligent Systems Transformational Models Transformational models are similar to stand-alone models in that the end result of development is an independent model that does not interact with others. What distinguishes the two types of models is that transformational systems begin as one type of system and end up as the other. Transformational models offer several benefits to developers.
Agent-Based Hybrid Intelligent Systems: An Agent-Based Fromework for Complex Problem Solving by Zhang Ch.