Subject description - A4M33PAH
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Explanatory Notes
Instructions
| A4M33PAH |
Planning and game playing |
| Roles: | |
Extent of teaching: | 2P+2C |
| Department: | 13136 |
Language of teaching: | CS |
| Guarantors: | |
Completion: | Z,ZK |
| Lecturers: | |
Credits: | 6 |
| Tutors: | |
Semester: | L |
Anotation:
This course provides an introduction to classical AI planning (linear, nonliner planning, graph-plan planning, heuristic planning, SAT-based planning) and game-tree representation and methods of adversarial search (such as minimax and alpha/beta pruning).
Course outlines:
| 1. | | planning problem representation and planning problem komplexity |
| 2. | | linear planning, TOPLAN algorithm, |
| 3. | | nonlineární planning, causal links thread resolution |
| 4. | | Graf-oriented planning |
| 5. | | planning by means of SAT |
| 6. | | Introduction to game playing |
| 7. | | Minimax, alfa-beta prunning |
| 8. | | Advenced methods of adversarial planning |
| 9. | | Hierarchical HTN planning |
| 10. | | Heuristic planning |
| 11. | | Contingency planning, temporal planning |
| 12. | | Planning a probability |
| 13. | | Planning in game playing |
Exercises outline:
| 1. | | Planning problems |
| 2. | | Semestral project specification: design and development of a general planner |
| 3. | - | 5. Laboratories |
| 6. | | Game playing algorithms |
| 7. | | Semestral project specification: design and development of a game playing algorithm |
| 8. | - | 12. Laboratories |
| 13. | | Competition |
Literature:
Nau, D., Ghallab, M., and Traverso, P. 2004 Automated Planning: Theory
and Practice. Morgan Kaufmann Publishers Inc.
Russell, S. J. and Norvig, P. 2003 Artificial Intelligence: a Modern
Approach. 2. Pearson Education.
Requirements:
Subject is included into these academic programs:
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Recommended semester |
| Page updated 16.4.2024 12:52:56, semester: L/2023-4, Z/2024-5, Z/2023-4, Send comments about the content to the Administrators of the Academic Programs |
Proposal and Realization: I. Halaška (K336), J. Novák (K336) |