Learning Algorithms Theory and Applications

1.Theory.- 1. Introduction.- 1.1. Various Approaches to Learning.- 1.2. A Learning Algorithm.- 1.3. Performance Measures and Statement of Problem.- 1.4. Classification of Learning Algorithms.- 1.5. Organization of the Book.- 1.6. Comments and Historical Remarks.- 1.7. Exercises.- 2. Ergodic Learning Algorithms.- 2.1. Introduction.- 2.2. NER?P - Algorithm.- 2.3. Analysis.- 2.4. An Alternate Characterization of z(k).- 2.5. Simulations (M = 2).- 2.6. Analysis and Simulations: General Case M ? 2.- 2.7. Comments and Historical Remarks.- 2.8. Appendix.- 2.9. Exercises.- 3. Absolutely Expedient Learning Algorithms.- 3.1. Introduction.- 3.2. NAR?P Algorithm.- 3.3. Conditions for Absolute Expediency.- 3.4. Analysis of Absolutely Expedient Algorithms.- 3.5. An Algorithm to ComDute Bounds.- 3.6. Absolute Expediency and ?-Optimality.- 3.7. Simulations.- 3.8. Comments and Historical Remarks.- 3.9. Appendix 9.- 3.10. Exercises.- 4. Time Varying Leading Algorithms.- 4.1. Introduction.- 4.2. A Time Varying Learning Algorithm.- 4.3. Kushner's Method of Asymptotic Analysis.- A. Convergence with Probability One.- B. Weak Convergence.- 4.4. Comments and Historical Remarks.- 4.5. Appendix.- 4.6. Exercises.- II. Applications.- 5. Two-Person Zero-Sum Sequential, Stochastic Games with Imperfect and Incomplete Information-Game Matrix with Saddle-Point in Pure Strategies.- 5.1. Introduction.- 5.2. The LAR?P - Algorithm and Statement of Results.- 5.3. Analysis of Games.- 5.4. Special Case - Dominance.- 5.5. Simulations.- 5.6. Comments and Historical Remarks.- 5.7. Appendix.- 5.8. Exercises.- 6. Two-Person Zero-Sum Sequential, Stochastic Games with Imperfect and Incomplete Information - General Case.- 6.1. Introduction.- 6.2. LER?P Algorithm.- 6.3. Analysis of Game.- 6.4. Extensions.- 6.5. Simulations.- 6.6. Comments and Historical Remarks.- 6.7. Appendix.- 6.8. Exercises.- 7. Two-Person Decentralised Team Problem with Incomplete Information.- 7.1. Introduction.- 7.2. Analysis of Decentralised Team Problem LER?P Algorithm.- 7.3. Analysis of Decentralised Team Problem LAR?IAlgorithm.- 7.4. Simulations.- 7.5. Comments and Historical Remarks.- 7.6. Exercises.- 8. Control of a Markov Chain with Unknown Dynamics and Cost-Structure.- 8.1. Introduction.- 8.2. Definitions and Statement of Problem.- 8.3. Learning Algorithm.- 8.4. Analysis.- 8.5 Simulations.- 8.6. Extension to Delayed State Observations.- 8.7. Comments and Historical Remarks.- 8.8. Exercises.- Epilogue.- Epilogue.- References.