1. traditional game playing 2 player adversarial (win => lose) based on search but... huge game trees can't be fully explored

2. traditional game playing 2 player adversarial (win => lose) fixed rules – no general world kn based on search but... huge game trees – can't be fully explored why study them in AI? core part of tools & techniques adversary modelling is important economics, contingency planning & other areas

3. trad. game playing basics minimax search routine depth 1 st to fixed depth different approaches to copy, cache, states static evaluation fn assesses merit of game states for players simple +/- numeric value alpha-beta pruning std approach for reducing game trees

4. alpha – beta … continued best & worst cases improving alpha-beta simple ordering fns eg: captures => threats => moves

5. other strategies (growth, etc) eval all nodes & extend tree heuristic growth quiescence plausibility  effort use different eval fns at different stages strategy, performance, etc library moves (open game / end game) state representations database lookup

6. other strategies (pruning) eval all nodes & prune tree heuristic pruning limiting breadth futility cut-off caching states when/why to cache cache persistence => library moves?

7. minimum needs 1.a state representation 2.a static evaluation fn 3.a legal move generator 4.minimax 5.alpha-beta pruning?

8. uncertainty 1.chance – dice games, etc 2.incomplete kn – cards, etc