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| Semester | fall semester 2023 |
| Course frequency | Every fall sem. |
| Lecturers |
Malte Helmert (malte.helmert@unibas.ch, Assessor)
Gabriele Röger (gabriele.roeger@unibas.ch) |
| Content | The course provides an introduction to the theory and algorithms for automated planning, with an emphasis on classical planning. Automated planning is concerned with determining a sequence of actions that transforms a given initial state into a desirable state in a very large state space. Topics covered include: planning formalisms and normal forms; progression and regression; computational complexity of planning; heuristics for classical planning based on delete relaxation, abstraction, landmarks, critical paths and network flows; formal relationships between heuristics. |
| Learning 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. |
| Bibliography | 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. |
| Weblink | course web page |
| Admission requirements | 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. Most importantly, good knowledge of the contents of the courses "Theory of Computer Science" (10948) and "Foundations of Artificial Intelligence" (13548) is assumed, in particular the topic of NP-completeness from the theory course and the topics of state-space search and propositional logic from the AI course. Students who have not previously passed the prerequisite courses 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 sufficient knowledge of these topics, we strongly encourage you to contact the lecturers prior to the semester to discuss a possible self-study plan. |
| Course application | Registration via https://services.unibas.ch. |
| Language of instruction | English |
| Use of digital media | Online, mandatory |
| Course auditors welcome |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Monday | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| wöchentlich | Wednesday | 14.15-16.00 | Spiegelgasse 1, Seminarraum 00.003 |
| Modules |
Doctorate Computer Science: Recommendations (PhD subject: Computer Science) Modul: Concepts of Machine Intelligence (Master's degree subject: Computer Science) Module: Applications of Distributed Systems (Master's Studies: Computer Science) Module: Concepts of Machine Intelligence (Master's Studies: Computer Science) Module: Electives in Data Science (Master's Studies: Data Science) Module: Methods of Machine Intelligence (Master's Studies: Computer Science) |
| Assessment format | continuous assessment |
| Assessment details | Marked homework exercises will be handed out in order to support and assess the learning progress. To qualify for the written 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 written examination. Expected date: Wednesday, January 24, 2024, 2-4 p.m. |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,5 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Science, studiendekanat-philnat@unibas.ch |
| Offered by | Fachbereich Informatik |