Chapter 3B References

Books

• Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach. Minimax search, alpha-beta pruning, game playing, probabilistic reasoning, and Bayesian networks.
• Michael Sipser, Introduction to the Theory of Computation. Finite trees, exhaustive search, and the computational limits of exact methods.
• Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction. Sequential decision making, value functions, policies, and Monte Carlo methods.
• Thomas M. Cover and Joy A. Thomas, Elements of Information Theory. Probability, entropy, coding, and the information-theoretic view of inference.
• David J. C. MacKay, Information Theory, Inference, and Learning Algorithms. Bayesian inference, graphical models, and variational methods.
• Kevin P. Murphy, Machine Learning: A Probabilistic Perspective. Bayesian models, graphical models, and probabilistic prediction.

Websites

• Stanford Encyclopedia of Philosophy: Game Theory — strategic games, minimax, and mixed strategies.
• Stanford Encyclopedia of Philosophy: Probability — probability axioms and interpretations.
• scikit-learn: Naive Bayes — Gaussian, multinomial, and Bernoulli Naive Bayes models.
• pgmpy: Probabilistic Graphical Models using Python — Bayesian networks, factor graphs, and inference algorithms.
• MCTS paper by Levente Kocsis and Csaba Szabó — the bandit-based selection rule for Monte Carlo tree search.
• Alpha-beta pruning — a concise description of the bounds used to discard game-tree branches.