Students are able to represent typical planning problems in general formalisms like PDDL, understand the computational methods, particular advantages as well as disadvantages of common planning systems.

Practical exercises and projects based on the concepts introduced in lecture sessions

The course presents basic and advanced concepts of modern general-purpose planning systems, ranging from algorithmic methods and translation techniques to scheduling aspects. Topics to investigate include representation formalisms for expressing planning problems, such as the planning domain definition language (PDDL). We will inspect and exercise complementary solving algorithms and heuristics of corresponding domain-independent planning systems, which utilize state-space search methods or translate planning problems into constraint-based formalisms like ASP, CSP and SAT.

Basic knowledge of propositional and first-order logic as well as fundamental concepts of artificial intelligence, knowledge representation and reasoning

Haslum, P., Lipovetzky, N., Magazzeni, D., Muise, C.: An Introduction to the Planning Domain Definition Language. Morgan and Claypool, 2019.Helmert, M.: The Fast Downward Planning System. Journal of Artificial Intelligence Research 26: 191–246, 2006.Dimopoulos, Y., Gebser, M., Lühne, P., Romero, J., Schaub, T.: plasp 3: Towards Effective ASP Planning. Theory and Practice of Logic Programming 19(3): 477-504, 2019.Rintanen, J.: Planning as Satisfiability: Heuristics. Artificial Intelligence 193: 45-86, 2012.Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer Set Solving in Practice. Morgan and Claypool, 2012.

three practical assignments using domain-independent planning systems

modeling and solving of planning problems based on the planning domain definition language

correctness and comprehensiveness of supplied solutions to practical tasks