Titlebook: Deep Fusion of Computational and Symbolic Processing; Takeshi Furuhashi,Shun’Ichi Tano,Hans-Arno Jacobse Book 2001 Springer-Verlag Berlin

大约冬季

A Generic Architecture for Hybrid Intelligent Systems a unifying paradigm, well known in the artificial intelligence community. This paradigm serves us as conceptual framework to better understand, modularize, compare, and evaluate the individual approaches. We think it is crucial for the design of intelligent systems to focus on the integration and i

Autobiography

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Irritate

Integration of Different Information Processing Methodsse methods is studied. A special processor to combine different methods is necessary for integration. It is called an integrator. Among various information-processing methods, only declarative knowledge-based method is suited for an integrator. Then the realistic way of developing the integrator is

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Symbol Pattern Integration Using Multilinear Functions with by logical reasoning. The typical pattern processing is neural networks, where patterns are dealt with by numerical computation. The integration of symbols and patterns means numerical computation of symbols and logical reasoning of patterns, that is, pattern reasoning. The key is the multilin

Fortuitous

Design of Autonomously Learning Controllers Using ,rement: without knowledge of a process model the system learns a control policy. Optimization goals like time-optimal or energy-optimal control as well as restrictions of allowed manipulated variables or system states can be defined in a simple and flexible way. . only learns on basis of success and

TEM

Modeling for Dynamic Systems with Fuzzy Sequential Knowledge “fuzzy” sequential knowledge for the description of dynamic characteristics of a system. Symbolic Dynamic System(SDS), a model for symbolic sequences, is extended to deal with “fuzzy” symbolic sequences. This approach introduces topological nature into the symbolic sequences, which allows an interp

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Hybrid Machine Learning Tools: INSS — A Neuro-Symbolic System for Constructive Machine Learning comparison with its predecessor because the learning and the knowledge extraction process are faster and are accomplished in an incremental way . INSS offers a new approach applicable to constructive machine learning with high-performance tools, even in the presence of incomplete or erroneous data.

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A Generic Architecture for Hybrid Intelligent Systemshrough hybridization or fusion, has in recent years contributed to a large number of new intelligent system designs. Many of these approaches, however, follow an ad hoc design methodology, further justified by success in certain application domains. Due to the lack of a common framework it remains o