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AI Planning is one of the fundamental sub-areas of Artificial Intelligence, concerned with algorithms that can generate strategies of action for arbitrary autonomous agents in arbitrary environments. The course will address so-called classical planning, where the actions and environment are assumed to be deterministic; this is a central area in planning, and has been the source of many influential ideas. It is also successfully applied in practice, as we will exemplify in the course. We will examine the technical core of the current research on solving this kind of problem. We will consider four different paradigms for automatically generating heuristic functions (lower bound solution cost estimators): critical paths, ignoring delete lists, abstractions, landmarks. Apart from understanding these techniques themselves, we will learn how to analyze, combine, and compare such estimators. We will furthermore consider optimality-preserving pruning techniques based on partial-order reduction, symmetries, and dominance pruning. The course contains many research results from the last decade, close to the current research frontier in planning.
Prerequisites. Ideally, participating students should have successfully completed an introductory course in Artificial Intelligence. However, the course is self-contained and any student with a solid basis in Computer Science -- algorithms, data structures, programming, propositional logic, NP-hardness -- should in principle be able to follow. Prior knowledge about search (the A* algorithm etc) is an advantage. Students who have already passed Automatic Planning in previous years are not allowed to attend the course.