This course provides an introduction to the field of artificial intelligence (AI). The major topics covered include reasoning and representation, search, constraint satisfaction problems, planning, logic, reasoning under uncertainty, and planning under uncertainty.
By the end of this course students should be able to
- explain what AI is and what it can and cannot do
- discuss the nature of fundamental problem types (constraint satisfaction, query/inference, and planning) and environment types (deterministic and stochastic)
- discuss core representation and reasoning (R&R) systems in AI, including (but not limited to)
- informed search
- local search and its stochastic variants
- planning representations such as STRIPS
- propositional definite clause logic
- probabilistic reasoning using Bayesian networks and other graphical models
- apply appropriate R&R systems to solve problems based on the problem type and environment
The course material is publicly available.