Archive: Fall Semester 2017
Lecture: Planning and Optimization
Course Number | 45400-01 |
Lecturers |
Malte Helmert
Gabriele Röger |
Assistants | Florian Pommerening |
Tutors |
Salome Eriksson
Cedric Geissmann |
Time and Location |
Mon 14:15 - 16:00; Seminarraum 00.003, Spiegelgasse 1
Wed 14:15 - 16:00; Seminarraum 00.003, Spiegelgasse 1 |
Start | 20-09-2017 |
Exercises | Wed 16:15 - 18:00; Seminarraum 05.001, Spiegelgasse 5 |
Prerequisites |
Good knowledge in the foundations and core areas of computer science are assumed, in particular algorithms and data structures, complexity theory, mathematical logic and programming.
Good knowledge of the contents of the course "Foundations of Artificial Intelligence" (13548) is assumed, in particular the chapters on state-space search. Students who have not previously passed the prerequisite course are strongly advised to learn the necessary material in self-study prior to the beginning of this course. If you are interested in participating in this course but do not yet have strong knowledge on state-space search, we strongly encourage you to contact the lecturers prior to the semester to discuss a possible self-study plan. |
Objectives | The participants get to know the theoretical and algorithmic foundations of action planning as well as their practical implementation. They understand the fundamental concepts underlying modern planning algorithms as well as the theoretical relationships that connect them. They are equipped to understand research papers and conduct projects in this area. |
Contents |
The course provides an introduction to the theory and algorithms for classical planning, with an emphasis on heuristic search methods. Classical planning is concerned with finding action sequences (plans) that transform a given initial state into a state satisfying a goal condition in very large state spaces. Topics covered include: planning formalisms and normal forms; progression and regression; computational complexity of planning; planning heuristics based on delete relaxation, abstraction, critical paths, landmarks and network flows; theoretical connections between planning heuristics and the concept of cost partitioning; symbolic search.
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Literature | There is no textbook for the course. The course slides will be made available to the participants, and additional research papers complementing the course materials will be uploaded to the course webpage during the semester. |
Assessment |
Lehrveranst.-begleitend
Please note : Oral examination Dates: Monday, 5 February; Tuesday, 6 February; Wednesday, 7 February Room: Office 06.004 Marked homework exercises will be handed out weekly in order to assess the learning progress. To qualify for the oral examination, students must obtain at least 50% of the total marks from the exercises. Exercise marks do not contribute to the final grade for the course, which is exclusively based on the oral examination. |
Credit Points | 8 |
Grades | 1-6 0,5 |
Modules |
Modul Kerninformatik (MSF - Informatik (Studienbeginn vor 01.08.2016))
Modul Kerninformatik (Master Informatik 10) Wahlbereich Master Informatik: Empfehlungen (Master Informatik 10) Modul Praxis aktueller Informatikmethoden (MSF - Informatik (Studienbeginn vor 01.08.2016)) Modul Applications of Distributed Systems (Master Computer Science 16) Modul Concepts of Machine Intelligence (Master Computer Science 16) Modul Concepts of Machine Intelligence (MSF - Computer Science) |
Registration | Services (Requires login) |
Lecture Slides
No. | Topic | Date | Slides |
A1 | Organizational Matters | 20.09.2017 | |
A2 | What is Planning? | 20.09.2017 | |
X1 | Hands-On and Repetition | 25.09.2017 | |
X2 | Hands-On and Repetition | 27.09.2017 | |
A3 | Transition Systems and Propositional Logic | 02.10.2017 | |
A4 | Propositional Planning Tasks | 02.10.2017 | |
A5 | Equivalent Operators and Effect Normal Form | 04.10.2017 | |
A6 | Positive Normal Form and STRIPS | 04.10.2017 | |
A7 | Invariants and Mutexes | 09.10.2017 | |
A8 | Finite Domain Representation | 09.10.2017 | |
B1 | Planning as Search | 11.10.2017 | |
B2 | Regression: Introduction & STRIPS Case | 11.10.2017 | |
B3 | General Regression, Part I | 16.10.2017 | |
B4 | General Regression, Part II | 16.10.2017 | |
B5 | Computational Complexity of Planning: Background | 18.10.2017 | |
B6 | Computational Complexity of Planning: Results | 18.10.2017 | |
C1 | Delete Relaxation: Introduction | 23.10.2017 | |
C2 | Delete Relaxation: Properties & Finding Relaxed Plans | 23.10.2017 | |
C3 | Delete Relaxation: AND/OR Graphs | 25.10.2017 | |
C4 | Delete Relaxation: Relaxed Task Graphs | 25.10.2017 | |
C5 | Delete Relaxation: hmax and h^add | 30.10.2017 | |
C6 | Delete Relaxation: Best Achievers and h^FF | 30.10.2017 | |
D1 | Abstractions: Introduction | 01.11.2017 | |
D2 | Abstractions: Formal Definition and Heuristics | 01.11.2017 | |
D3 | Abstractions: Additive Abstractions | 06.11.2017 | |
D4 | Pattern Databases: Introduction | 06.11.2017 | |
D5 | Pattern Databases: Multiple Patterns | 08.11.2017 | |
D6 | Pattern Databases: Pattern Selection | 08.11.2017 | |
D7 | Merge-and-Shrink Abstractions: Synchronized Product | 13.11.2017 | |
D8 | M&S: Generic Algorithm and Heuristic Properties | 13.11.2017 | |
D9 | M&S: Maintaining the Abstraction and Shrinking Strategies | 15.11.2017 | |
D10 | M&S: Merging Strategies and Label Reduction | 15.11.2017 | |
E1 | Critical Path Heuristics: h^m | 20.11.2017 | |
E2 | Critical Path Heuristics: Properties and Pi^m Compilation | 20.11.2017 | |
E3 | Landmarks: Introduction & Minimum Hitting Set Heuristic | 22.11.2017 | |
E4 | Landmarks: Cut Landmarks & LM-cut Heuristic | 22.11.2017 | |
E5 | Landmarks: And/Or Landmarks | 27.11.2017 | |
E6 | Landmarks: LM-count Heuristic | 27.11.2017 | |
E7 | Linear & Integer Programming | 29.11.2017 | |
E8 | Flow Heuristic | 29.11.2017 | |
F1 | Cost Partitioning: Definition, Properties, and Abstractions | 04.12.2017 | |
F2 | Cost Partitioning: Landmarks and Generalization | 04.12.2017 | |
F3 | Post-hoc Optimization & Operator Counting | 06.12.2017 | |
F4 | Potential Heuristics & Connections | 06.12.2017 | |
F5 | Comparison of Heuristic Families I | 11.12.2017 | |
F6 | Comparison of Heuristic Families II | 11.12.2017 | |
G1 | Symbolic Search: BDDs | 13.12.2017 | |
G2 | Symbolic Search: BDD Operations and Breadth-First Search | 13.12.2017 | |
G3 | Symbolic Search: Uniform-cost and A* search | 18.12.2017 |