Introduction to Artificial Intelligence

• Explain, apply and implement uninformed and informed search techniques to solve AI problems
• Explain, apply and implement adversarial search techniques for AI in games
• Design, implement, benchmark and analyse search heuristics and game evaluation functions
• Explain and apply logical techniques for the representation of knowledge and reasoning
• Explain, apply and implement inference techniques in propositional and first-order logic
• Explain the logical methods of knowledge-base update
• Explain the strength and weaknesses of different AI techniques within search and logic
• Correctly determine the relevant AI technique for solving a given problem
• Explain the scope of AI, its potential for society as well as its limitations
• Discuss philosophical, ethical, legal, and societal consequences of AI

What is AI? Foundations of AI. Uninformed search. Informed/heuristic search: A*, greedy best-first search. Adversarial search: game trees, Minimax, Monte-Carlo Tree Search. Knowledge bases in propositional and first-order logic. Inference techniques in propositional and first-order logic: modus ponens, forward and backward chaining, resolution, model-checking, SAT-solving. Basic strategies for updating knowledge bases (belief revision). Philosophical, ethical, legal and societal aspects of AI.

02100/02101/02102/02312/02314/02105/01017/01019, or equivalent courses. You need experience with algorithms and data structures, programming, propositional logic and recursive procedures. The course requires experience with implementing non-trivial algorithms in a standard programming language like Java or Python. The course can be taken by bachelor students satisfying the recommended prerequisites.

Lectures, exercises, and written assignments.