CA300 - Introduction to Artificial Intelligence
Practical - Tyrrell's world
- Search and Learning - state spaces,
- Learning from examples - Single-layer Neural Networks,
Multi-layer Neural Networks, supervised learning, back-propagation,
Neural Networks as generalisations (of state-spaces or non-linear functions).
- Learning from rewards - Reinforcement Learning, Delayed reinforcement,
Markov worlds, exploration v. exploitation, world models.
- Artificial evolution - fitness landscapes, hill-climbing, the Genetic Algorithm,
classifier systems, Genetic Programming.
- Collective behavior - Cellular Automata, Chaos and Complexity, basins and attractors,
prediction, the Game of Life, Artificial Life.