Planning and game playing

Semestr: Summer

Range: 2+2c

Completion:

Credits: 6

Programme type: Master

Study form: Fulltime

Course language:

Time table at FEE

Summary:

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).

Keywords:

Course syllabus:

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

Seminar syllabus:

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.

Examiners:

Lecturers:

Instructors: