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基本説明
Considers synthesis of complex learning structures from simple building blocks and uses stochastic algorithms for refining probabilities of selecting actions.
Full Description
Networks of Learning Automata: Techniques for Online Stochastic Optimization is a comprehensive account of learning automata models with emphasis on multiautomata systems. It considers synthesis of complex learning structures from simple building blocks and uses stochastic algorithms for refining probabilities of selecting actions. Mathematical analysis of the behavior of games and feedforward networks is provided. Algorithms considered here can be used for online optimization of systems based on noisy measurements of performance index. Also, algorithms that assure convergence to the global optimum are presented. Parallel operation of automata systems for improving speed of convergence is described. The authors also include extensive discussion of how learning automata solutions can be constructed in a variety of applications.
Contents
1. Introduction.- 1.1 Machine Intelligence and Learning.- 1.2 Learning Automata.- 1.3 The Finite Action Learning Automaton (FALA).- 1.4 Some Classical Learning Algorithms.- 1.5 The Discretized Probability FALA.- 1.6 The Continuous Action Learning Automaton (CALA).- 1.7 The Generalized Learning Automaton (GLA).- 1.8 The Parameterized Learning Automaton (PLA).- 1.9 Multiautomata Systems.- 1.10 Supplementary Remarks.- 2. Games of Learning Automata.- 2.1 Introduction.- 2.2 A Multiple Payoff Stochastic Game of Automata.- 2.3 Analysis of the Automata Game Algorithm.- 2.4 Game with Common Payoff.- 2.5 Games of FALA.- 2.6 Common Payoff Games of CALA.- 2.7 Applications.- 2.8 Discussion.- 2.9 Supplementary Remarks.- 3. Feedforward Networks.- 3.1 Introduction.- 3.2 Networks of FALA.- 3.3 The Learning Model.- 3.4 The Learning Algorithm.- 3.5 Analysis.- 3.6 Extensions.- 3.7 Convergence to the Global Maximum.- 3.8 Networks of GLA.- 3.9 Discussion.- 3.10 Supplementary Remarks.- 4. Learning Automata for Pattern Classification.- 4.1 Introduction.- 4.2 Pattern Recognition.- 4.3 Common Payoff Game of Automata for PR.- 4.4 Automata Network for Pattern Recognition.- 4.5 Decision Tree Classifiers.- 4.6 Discussion.- 4.7 Supplementary Remarks.- 5. Parallel Operation of Learning Automata.- 5.1 Introduction.- 5.2 Parallel Operation of FALA.- 5.3 Parallel Operation of CALA.- 5.4 Parallel Pursuit Algorithm.- 5.5 General Procedure.- 5.6 Parallel Operation of Games of FALA.- 5.7 Parallel Operation of Networks of FALA.- 5.8 Discussion.- 5.9 Supplementary Remarks.- 6. Some Recent Applications.- 6.1 Introduction.- 6.2 Supervised Learning of Perceptual Organization in Computer Vision.- 6.3 Distributed Control of Broadcast Communication Networks.- 6.4O ther Applications.- 6.5 Discussion.- Epilogue.- Appendices.- A The ODE Approach to Analysis of Learning Algorithms.- A.I Introduction.- A.2 Derivation of the ODE Approximation.- A.2.1 Assumptions.- A.2.2 Analysis.- A.3 Approximating ODEs for Some Automata Algorithms.- A.3.2 The CALA Algorithm.- A.3.3 Automata Team Algorithms.- A.4 Relaxing the Assumptions.- B Proofs of Convergence for Pursuit Algorithm.- B.1 Proof of Theorem 1.1.- B.2 Proof of Theorem 5.7.- C Weak Convergence and SDE Approximations.- C.I Introduction.- C.2 Weak Convergence.- C.3 Convergence to SDE.- C.3.1 Application to Global Algorithms.- C.4 Convergence to ODE.- References.
