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In recent years works done by most researchers towards building autonomous intelligent controllers frequently mention the need for a methodology of design and a measure of how successful the final result is. This monograph introduces a design methodology for intelligent controllers based on the analytic theory of intelligent machines introduced by Saridis in the 1970s. The methodology relies on the existing knowledge about designing the different sub-systems composing an intelligent machine. Its goal is to provide a performance measure applicable to any of the sub-systems, and use that measure to learn on-line the best among the set of pre-designed alternatives, given the state of the environment where the machine operates. Different designs can be compared using this novel approach.
Contents:
- Introduction
- Overview of the Design of Intelligent Control Systems
- Learning Stochastic Automata
- Hierarchical Intelligent Machines Revisited
- A Performance Measure for Intelligent Machines
- The Intelligent Machine as a Hierarchical Stochastic Automaton
- Design Procedure and Execution Algorithm
- Convergence Rate and Convergence Acceleration of Learning
- Examples and Case Studies
- Concluding Remarks
Readership: Control engineers and computer scientists (AI).
