Artificial Intelligence Planning

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Code Completion Credits Range Language
B4M36PUI Z,ZK 6 2P+2C Czech
The course cannot be taken simultaneously with:
Planning for Artificial Intelligence (BE4M36PUI)
Branislav Bošanský, Lukáš Chrpa, Antonín Komenda
Branislav Bošanský, Daniel Fišer, Leah Anusmita Chrestien, Lukáš Chrpa, Antonín Komenda
Department of Computer Science

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.

Syllabus of lectures:

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

Syllabus of tutorials:

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

Study Objective:
Study materials:

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

* https://www.coursera.org/course/aiplan

Further information:
Time-table for winter semester 2020/2021:
Time-table is not available yet
Time-table for summer semester 2020/2021:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2020-11-25
For updated information see http://bilakniha.cvut.cz/en/predmet4701306.html