Doctoral Thesis on Stochastic Learning Automata including Adaptation to Dynamic Markov Environments. The Learning Rules follow the "Bayesian Model" and the thesis successfully demonstrates the effectiveness of Self-Learning Automata. Topics include Stochastic Automata Games, Structured Automata and a short section on hierarchical Learning Automata. These topics are often referred to as Self-Organisation, Machine Learning & Artificial Intelligence. this approach to self-learning is now being applied to topics as diverse as cyber security, adaptive traffic routing, social media advertising and energy grid management. The author - Dr David Eric Probert - completed this thesis whilst at Churchill College & the Statistical :Laboratory at Cambridge University between 1973 to 1976.

1. The Evolution ofThe Evolution of
Stochastic AutomataStochastic Automata
David Eric ProbertDavid Eric Probert -- 19761976
Churchill College, CambridgeChurchill College, CambridgeChurchill College, CambridgeChurchill College, Cambridge

7. IntroductionIntroduction

19. Chapter OneChapter One
Unstructured Learning AutomataUnstructured Learning AutomataUnstructured Learning AutomataUnstructured Learning Automata

82. Chapter TwoChapter Two
Games between AutomataGames between AutomataGames between AutomataGames between Automata

101. Chapter ThreeChapter Three
Structured Learning AutomataStructured Learning AutomataStructured Learning AutomataStructured Learning Automata

156. BibliographyBibliography