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. Language: English