Adversarial Search and Alpha-Beta (W6)

Adversarial Search and Alpha-Beta Pruning

A. The problem with the minimax algorithm

B. Introduction to alpha and beta values

C. Alpha-beta pruning to optimize search

D. Strategies for optimizing search, such as recording best moves and using killer moves

Stochastic Games and Chance Nodes

A. Examples of stochastic games, like Backgammon

B. Introduction to chance nodes and incorporating probability

C. Probability theory and understanding permutations and chances in dice rolls

Uncertainty and Belief States

A. Dealing with uncertainty and probability in decision making

B. Introduction to Expectiminimax algorithm

C. Monte Carlo roll-out as a technique to estimate outcomes

Probability Theory and Utility

A. Importance of rational decision making

B. Evaluating relative importance of goals and likelihood of achieving them

C. Decision theory and expected utility

Markov Chains

A. Introduction to Markov chains and their probabilistic nature

B. Examples of Markov chains in various domains, such as weather and behavior

C. Transition matrices and calculating probabilities in Markov chains

Markov Chains

A. Brownian motion and random walks as examples

B. Markov Chain Monte Carlo (MCMC) analysis and its applications

C. Hidden Markov Models (HMM) and their uses in speech processing, genetics, and more

Applications of Markov Chains

A. Brownian motion and random walks as examples

B. Markov Chain Monte Carlo (MCMC) analysis and its applications

C. Hidden Markov Models (HMM) and their uses in speech processing, genetics, and more

Other Concepts

A. Absorbing Markov chains and games of chance

B. Chaos theory and its implications in deterministic processes

C. Examples of applications like Garkov and PHYLO