Subject description - B4M36PUI
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B4M36PUI | Artificial Intelligence Planning | ||
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Roles: | PO | Extent of teaching: | 2P+2C |
Department: | 13136 | Language of teaching: | CS |
Guarantors: | Pěchouček M. | Completion: | Z,ZK |
Lecturers: | Edelkamp S., Komenda A., Mrkos J., Urbanovská M. | Credits: | 6 |
Tutors: | Edelkamp S., Komenda A., Mrkos J., Urbanovská M. | Semester: | L |
Web page:
https://cw.fel.cvut.cz/b192/courses/be4m36pui/startAnotation:
The course covers the problematic of automated planning in artificial intelligence and focuses especially on domain independent models of planning problems: planning as a search in the space of states (state-space planning), in the space of plans (plan-space planning), heuristic planning, planning in graph representation of planning problems (graph-plan) or hierarchical planning. The students will also learn about the problematic of planning under uncertainty and the planning model as a decision-making in MDP and POMDP.Course outlines:
1. | Introduction to the problematic of automated planning in artificial intelligence | |
2. | Representation in form of search in the space of states (state-space planning) | |
3. | Heuristic planning using relaxations | |
4. | Heuristic planning using abstractions | |
5. | Structural heuristics | |
6. | The Graphplan algorithm | |
7. | Compilation of planning problems | |
8. | Representation of the planning problem in form of search in the space of plans (plan-space planning) | |
9. | Hierarchical planning | |
10. | Planning under uncertainty | |
11. | Model of a planning problem as a Markov Decision Process (MDP) | |
12. | Model of a planning problem as a Partially Observable Markov Decision Process (POMDP) | |
13. | Introduction to planning in robotics | |
14. | Applications of automated planning |
Exercises outline:
1. | Planning basics, representation, PDDL and planners | |
2. | State-space planning, Assignment 1 | |
3. | Relaxation heuristics, Assignment 1 Consultations | |
4. | Abstraction heuristics, Assignment 1 Deadline | |
5. | Landmark heuristics, Assignment 1 Results/0-point Deadline | |
6. | Linear Program formulation of heuristics | |
7. | Compilations | |
8. | Partial-order planning | |
9. | Hierarchical Planning | |
10. | Planning with uncertainty, Assignment 2 | |
11. | Planning for MDPs, Assignment 2 Consultations | |
12. | Planning for POMDPs, Assignment 2 Consultations | |
13. | Monte Carlo tree search, Assignment 2 Deadline | |
14. | Consultations of exam topics, Assignment 2 Results/0-point Deadline, Credit |
Literature:
* Malik Ghallab, Dana Nau, Paolo Traverso: Automated Planning: Theory & Practice, Elsevier, May 21, 2004 * https://www.coursera.org/course/aiplanRequirements:
Subject is included into these academic programs:Program | Branch | Role | Recommended semester |
MPOI7_2018 | Artificial Intelligence | PO | 2 |
Page updated 7.6.2023 17:50:17, semester: L/2022-3, Z/2023-4, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |