Artificial Intelligence in Games

1. Board games (2 lectures)
- Game theory
- Minmax Search
- Alpha-Beta Search
- Monte Carlo Tree Search

2. Movement (2 lectures)
- Steering behaviors
- Kinematic movement algorithms
- Game physics
- Coordinated movement

3. Pathfinding (2 lectures)
- Bread-first and Depth-First search algorithms
- Dijkstra
- A* - IDA (Iterative Deepening A*), SMA* (Simplified Memory-Bounded A*
- Hierarchical pathfinding
- Multi-agent pathfinding
- Flood fill algorithm

4. Decision making (2 lectures)
- Spatial data structures for (faster) Collision calculations – Multi-resolution maps, quad or octrees, binary space partition - BSP) trees
- Decision trees – ID3, C4.5 CART (Classification And Regression Tree), CHAID (Chi-square automatic interaction detection), MARS (multivariate adaptive regression splines)
- State machines, Stack-based finite state machines
- Fuzzy logic

5. Tactical and strategic AI (2 lectures)
- Waypoint tactics – influence maps
- Tactical analysis – map flooding, convolution filters, Gaussian blur
- Tactical Pathfinding – structuring multi-tier AI
- Coordinated actions

6. Learning (3 lectures)
- Learning basics – N-grams, string matching
- Parameter modification
- Action prediction
- Reinforcement learning