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45400-01 - Vorlesung: Planning and Optimization 8 KP

Semester Herbstsemester 2021
Angebotsmuster Jedes Herbstsemester
Dozierende Malte Helmert (malte.helmert@unibas.ch, BeurteilerIn)
Gabriele Röger (gabriele.roeger@unibas.ch)
Inhalt The course provides an introduction to the theory and algorithms for automated planning, with an emphasis on classical and probabilistic planning. Automated planning is concerned with determining a sequence of actions or policy 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; probabilistic planning via dynamic programming, heuristic search and Monte-Carlo tree search.
Lernziele 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.
Literatur 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


Teilnahmebedingungen 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 courses "Theory of Computer Science" (10948) and "Foundations of Artificial Intelligence" (13548) is assumed, in particular the topics of propositional logic and NP-completeness from the theory course and the topic of state-space search 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.
Anmeldung zur Lehrveranstaltung Registration via https://services.unibas.ch.
Unterrichtssprache Englisch
Einsatz digitaler Medien Online-Veranstaltung
HörerInnen willkommen


Intervall wöchentlich
Datum 22.09.2021 – 26.01.2022
Zeit Mittwoch, 14.15-16.00 - Online Präsenz -
Datum Zeit Raum
Mittwoch 22.09.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 29.09.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 06.10.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 13.10.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 20.10.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 27.10.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 03.11.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 10.11.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 17.11.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 24.11.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 01.12.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 08.12.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 15.12.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 22.12.2021 14.15-16.00 Uhr - Online Präsenz -, --
Mittwoch 26.01.2022 14.00-16.00 Uhr Alte Universität, Hörsaal -101
Module Doktorat Informatik: Empfehlungen (Promotionsfach Informatik)
Modul: Applications of Distributed Systems (Masterstudium: Computer Science)
Modul: Concepts of Machine Intelligence (Masterstudium: Computer Science)
Modul: Concepts of Machine Intelligence (Master Studienfach Computer Science)
Leistungsüberprüfung Lehrveranst.-begleitend
Hinweise zur Leistungsüberprüfung Written examination
expected date: Wednesday, 26 January 2022, 14:00-16:00, Alte Uni, Hörsaal -101.

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.
An-/Abmeldung zur Leistungsüberprüfung An-/Abmelden: Belegen resp. Stornieren der Belegung via MOnA
Wiederholungsprüfung keine Wiederholungsprüfung
Skala 1-6 0,5
Wiederholtes Belegen beliebig wiederholbar
Zuständige Fakultät Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch
Anbietende Organisationseinheit Fachbereich Informatik