Participation
Please register for the course to gain access to the Adam workspace.
Lecture Slides
| No. | Topic | Date | Slides |
|---|---|---|---|
| A1 | Organizational Matters | 18.09. | printer handout screen |
| A2 | What is Planning? | 18.09. | printer handout screen |
| A3 | Getting to Know a Planner | 23.09. | printer handout screen |
| B1 | Transition Systems and Propositional Logic | 23.09. | printer handout screen |
| B2 | Introduction to Planning Tasks | 25.09. | printer handout screen |
| B3 | Formal Definition of Planning | 25.09. | printer handout screen |
| B4 | Equivalent Operators and Normal Forms | 30.09. | printer handout screen |
| B5 | Positive Normal Form and STRIPS | 30.09. | printer handout screen |
| B6 | Computational Complexity of Planning | 02.10. | printer handout screen |
| C1 | Overview of Classical Planning Algorithms | 07.10. | printer handout screen |
| C2 | Progression and Regression Search | 09.10. | printer handout screen |
| C3 | General Regression | 09.10. | printer handout screen |
| C4 | SAT Planning: Core Idea and Sequential Encoding | 14.10. | printer handout screen |
| C5 | SAT Planning: Parallel Encoding | 14.10. | printer handout screen |
| C6 | Symbolic Search: Binary Decision Diagrams | 16.10. | printer handout screen |
| C7 | Symbolic Search: Full Algorithm | 16.10. | printer handout screen |
| D1 | Delete Relaxation: Relaxed Planning Tasks | 21.10. | printer handout screen |
| D2 | Delete Relaxation: Properties of Relaxed Planning Tasks | 21.10. | printer handout screen |
| D3 | Delete Relaxation: Finding Relaxed Plans | 23.10. | printer handout screen |
| D4 | Delete Relaxation: AND/OR Graphs | 23.10. | printer handout screen |
| D5 | Delete Relaxation: Relaxed Task Graphs | 28.10. | printer handout screen |
| D6 | Delete Relaxation: hmax and hadd | 28.10. | printer handout screen |
| D7 | Delete Relaxation: Analysis of hmax and hadd | 30.10. | printer handout screen |
| D8 | Delete Relaxation: hFF and Comparison of Heuristics | 30.10. | printer handout screen |
| E1 | Planning Tasks in Finite-Domain Representation | 04.11. | printer handout screen |
| E2 | Invariants and Mutexes | 04.11. | printer handout screen |
| E3 | Abstractions: Introduction | 06.11. | printer handout screen |
| E4 | Abstractions: Formal Definition and Heuristics | 06.11. | printer handout screen |
| E5 | Abstractions: Additive Abstractions | 11.11. | printer handout screen |
| E6 | Pattern Databases: Introduction | 11.11. | printer handout screen |
| E7 | Pattern Databases: Multiple Patterns | 13.11. | printer handout screen |
| E8 | Pattern Databases: Pattern Selection | 13.11. | printer handout screen |
| E9 | Merge-and-Shrink: Factored Transition Systems | 18.11. | printer handout screen |
| E10 | Merge-and-Shrink: Algorithm | 18.11. | printer handout screen |
| E11 | Merge-and-Shrink: Properties and Shrink Strategies | 20.11. | printer handout screen |
| E12 | Merge-and-Shrink: Merge Strategies and Label Reduction | 20.11. | printer handout screen |
Extra Material
| No. | Title | Files |
|---|---|---|
| B6 | Tom Bylander. The computational complexity of propositional STRIPS planning. Artificial Intelligence, 69(1-2), pp. 165-204, 1994. | |
| C1/D8 | Jörg Hoffmann and Bernhard Nebel. The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research, 14, pp. 253-302, 2001. | |
| C1 | Silvia Richter and Matthias Westphal. The LAMA planner: Guiding cost-based anytime planning with landmarks. Journal of Artificial Intelligence Research, 39, pp. 127-177, 2010. | |
| C3 | Jussi Rintanen.Regression for Classical and Nondeterministic Planning. Proc. ECAI 2008, pp. 568-572, 2008. | |
| C5 | Jussi Rintanen, Keijo Heljanko, and Ilkka Niemelä.Planning as satisfiability: parallel plans and algorithms for plan search. Artificial Intelligence, 170(12-13), pp. 1031-1080, 2006. | |
| C7 | Álvaro Torralba. Symbolic Search and Abstraction Heuristics for Cost-Optimal Planning. PhD thesis, 2015. | |
| C7 | David Speck. Symbolic Search for Optimal Planning with Expressive Extensions. PhD thesis, 2022. | |
| D6 | Blai Bonet and Hector Geffner. Planning as Heuristic Search. Artificial Intelligence, 129(1), pp. 5-33, 2001. | |
| D8 | Emil Keyder and Hector Geffner. Heuristics for Planning with Action Costs Revisited. ECAI 2008, pp. 588-592, 2008. | |
| E1 | Jussi Rintanen. An Iterative Algorithm for Synthesizing Invariants. Proc. AAAI 2000, pp. 806-811, 2000. | |
| E2 | Daniel Fišer and Antonín Komenda. Fact-Alternating Mutex Groups for Classical Planning. Journal of Artificial Intelligence Research, 61, pp. 475-521, 2018. | |
| E8 | Stefan Edelkamp. Planning with Pattern Databases. Proc. ECP 2001, pp. 13-24, 2001. | |
| E8 | Patrik Haslum, Blai Bonet and Héctor Geffner. New Admissible Heuristics for Domain-Independent Planning. Proc. AAAI 2005, pp. 1164-1168, 2005. | |
| E8 | Stefan Edelkamp. Automated Creation of Pattern Database Search Heuristics. Proc. MoChArt 2006, pp. 121-135, 2007. | |
| E8 | Patrik Haslum, Adi Botea, Malte Helmert, Blai Bonet and Sven Koenig. Domain-Independent Construction of Pattern Database Heuristics for Cost-Optimal Planning. Proc. AAAI 2007, pp. 1007-1012, 2007. | |
| E8 | Santiago Franco, Álvaro Torralba, Levi H. S. Lelis and Mike Barley. On Creating Complementary Pattern Databases. Proc. IJCAI 2017, pp. 4302-4309, 2017. |