A pattern recognition approach to intelligent behaviour: switching the strategies

TL;DR: Presented approach based on classifiers, as it is shown, is a useful tool to control non-linear plant with uncertainties and works faster which is desirable in Real Time System.

TL;DR: The neuro-fuzzy model provides a better fitness and allows inclusion of linguistic variables (such as colour, smell, taste, morphophysiology, etc.) and the accuracy is approximately equal to this achieved by a conventional neural network.

TL;DR: This paper presents an alternative approach of intelligent control: the pattern recognizing systems, with the main idea to endow the control system with the ability to learn from the experience generated by the interaction with the environment.

TL;DR: A model is proposed that integrates knowledge from research in both natural and artificial systems and consists of a hierarchical system architecture that performs behavior generation, world modeling, sensory processing, and value judgment.

TL;DR: It is shown that in some circumstances well-adapted individuals would be better advised to adopt simple but easily modifiable strategies suited for the current competitor strategies rather than incorporate complex and general strategies that may be effective against a wide range of opposing counter-strategies.

TL;DR: A case study on a hierarchically intelligent controlled prosthesis, summarized herein, establishes the feasibility of the suggested methodologies and the importance of a growing area in control engineering.

TL;DR: This work deals with the analytic formulation of the principle of increasing precision with decreasing intelligence; the fundamental principle of hierarchically intelligent control; a three-level structure representing the organization, coordination and execution of such a system.