Organisation
Time | only SS |
---|---|
Time | Friday, 10.00h-13.15h |
Room | 056 |
Credits | 4SWS/6ECTS |
Exam | written, 90min |
Announcements
- First lesson in SS 19: 22.03.2019
Contents
This lecture consists of 2 parts: A theoretical part, which introduces the AI-algorithm applied in game AI, and a practical part, where the application and implementaion of AI in modern computer games is demonstrated.
Pathfinding:
- Algorithms for pathfinding: A*, Hierarchical Pathfinding, Jump-Point-Search, Rectangular Symmetry Reduction,
- Monte Carlo Tree Search
- Heuristics for Indoor- and Outdoor-Pathfinding
- World Representations: Tile-Graphs, Nav-Meshes, Dirichlet-Domains
Decision Making and Planning:
- Decision Trees
- State Machines
- Behavior Trees
- Fuzzy Logic
- Goal Oriented Behavior
Solving Constrained Satisfaction Problems (CSP)
Tactical and Strategic AI:
- Waypoint Analysis
- Tactical Analysis
- Tactical Pathfinding
- Coordinated Action
Machine Learning in Computer Games:
- Categories and Applications in General
- Conventional Approaches for supervised Learning
Reinforcement Learning:
- Definitions, Categorization, Examples
- Markov Decision Process
- Modellbased Learning
- Q-Learning
- Exploitation/Exploration
Deep Reinforcement Learning:
- Q-Network
- AlphaGo
- AlphaZero
- AlphaStar
Procedural Content Generation:
- Genetic Algorithm
- Generative Adversarial Networks (GAN)
Structure, Contents, Documents
Lecture | Contents | Addtional Material |
---|---|---|
Introduction | AI in general, AI in Games | |
Pathfinding | Pathfinding algorithms for indoor- and outdoor environments, world representations | |
Decision Making I | Decision Trees, State Machines, Behavior Trees | |
Decision Making II | Fuzzy Logic, Goal Oriented Behavior, Goal Oriented Action Planning | |
Constraint Satisfaction Problems | Constraint Satisfaction Problems, Backtracking, Forward Checking, Constraint Propagation | A*-Example 8-Puzzle [.html] , A*-Example 8-Puzzle [.ipynb] , CSP-Example Sudoku [.html] , CSP-Example Sudoku [.ipynb] |
Tactical and Strategic AI | Waypoint Tactics, Tactical Analysis, Tactical Pathfinding | |
Monte Carlo Tree Search | ||
Reinforcement Learning | Value-Iteration, Q-Learning |