Principles of Artificial Intelligence Programming

Overview
The purpose of this course is to cover the major paradigms of Artificial Intelligence programming with a bias towards the application of these techniques to computer games. It is an advanced foundational course from which individual will be able to discover their own preferences and avenues for further exploration. In addition to covering the key AI programming paradigms the course also discusses

• selection of appropriate programming languages and representational schemes
• persistence , history and rollback to earlier states
• personality, emotion, motivation and psychology as they apply to computer games

During the course attendees will have access to a large library of books, software and research papers that they are encouraged to explore.

Key Skills

• Ability to choose between various approaches and frameworks for adding AI capabilities to computer games
• Insights into the complexity and performance tradeoffs of different AI technologies and techniques
• Techniques for developing games that learn and evolve

Course Contents
Overview of basic physics and behaviours

• movement algorithms, statics and kinematics
• searching and wandering

More complex movement behaviours

• Steering behaviours
• seek and flee
• pursue and evade
• path following
• collision avoidance
• obstacle and wall avoidance
• more complex, combined steering behaviours
• projectiles - aiming, shooting and trajectories
• jumping
• motor control and co-ordinated movement

Pathfinding

• Overview of graphs and graph theory
• basic graphs
• weighted directed and undirected graphs
• Dijkstra's algorithm
• A* algorithm

More advanced pathfinding

• hierarchical pathfinding
• interruptible pathfinding
• continuous time pathfinding
• movement planning

Decision making

• Decision trees
• State machines
• Fuzzy logic
• Markovian systems
• Goal seeking behaviour
• Rule based approaches
• Blackboard architectures

Tactics and Strategy

• Waypoints
• Tactical analysis and tactical pathfinding
• Coordination and coordinated action
• Diplomacy, negotiation and betrayal
• Emotions and Personalities - their tactical implications

Learning and Evolution

• Overview of learning algorithms and strategies
• Action predicition
• Decision tree learning
• Reinforcement learning
• Neural Networks
• Genetic Algorithms

Two player games and board games

• Minimaxing
• Indexing and Pattern Matching on Game States
• Iterative deepening and other optimisation approaches
• Turn based strategy games

Interface the AI to the Game World

• Knowing the right thing at the right time
• Searching for information
• Awareness of events
• Communication patterns between entities
• Senses and sensors
• The sensor data fusion problem
• The interaction between AI and content creation tools