Advanced Artificial Intelligence (HT 2018)

The Advanced Artificial Intelligence course focuses on two important tenets of Artificial Intelligence, namely Representation and Search. We explore how general uninformed and informed search techniques are used to solve combinatorial problems, and how problem structure can be leveraged to facilitate the search for a solution. The course aims to provide students with the following key capabilities:

• formulate real world problems as search problems, and sketch methods to solve them based on uninformed, heuristic and constraint-based search
• understand the computational bottlenecks of different problem solving algorithms
• decide the most appropriate algorithm for solving given problems
• understand how problem structure relates to the formal properties of the problem
• decide whether a given problem is tractable or requires exponential time for automated solving

Lecture material

Links to lecture slides will appear here as they are given in class.

• Part 1: Intelligent Agents
• Part 2: Problem Solving and Search: Uninformed Search Strategies
• Part 3: Problem Solving and Search: Informed Search Strategies
• Part 4: Constraint Reasoning, Backtracking Search
• Part 5: Boolean Satisfiability
• Part 6: Temporal Reasoning
• Part 6: Spatial Reasoning

Course Books

• S.J. Russell, P. and Norvig. "Artificial Intelligence: a Modern Approach" (3rd ed.), Pearson Publishing, 2010
• R. Dechter. "Constraint Processing", Morgan Kaufmann, 2003 (recommended)
• N.J. Nilsson. "Artificial Intelligence: A New Synthesis", Morgan Kaufmann, 1998 (recommended)