Subject description - B4M36PUI

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B4M36PUI Artificial Intelligence Planning
Roles:PO Extent of teaching:2P+2C
Department:13136 Language of teaching:CS
Guarantors:Pěchouček M. Completion:Z,ZK
Lecturers:Bošanský B., Chrpa L., Komenda A. Credits:6
Tutors:Bošanský B., Fišer D., Chrestien L., Chrpa L., Komenda A. Semester:L


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


* Malik Ghallab, Dana Nau, Paolo Traverso: Automated Planning: Theory & Practice, Elsevier, May 21, 2004 *



Subject is included into these academic programs:

Program Branch Role Recommended semester
MPOI7_2016 Artificial Intelligence PO 2
MPOI7_2018 Artificial Intelligence PO 2

Page updated 14.8.2020 17:51:49, semester: Z,L/2020-1, L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)