| 1 | Artificial Intelligence: attempt to definition, scope, main streams. Turing test. | |
| 2 | I | |
| 3 | Uniform cost search. Evaluation function vs. heuristic function in the space of candidate solutions. Greedy and A* search. | |
| 4 | Examples of applying neighborhood based search to problem solving: knapsack problem, finding paths in labyrinths, solving the 15-puzzle. | |
| 5 | Predicate logic. Resolution. | |
| 6 | Inference as search task. | |
| 7 | Applying search methods for the inference. Goal-driven and data-driven inference. PROLOG. Example predicates - see Hanoi towers by myself and by Wikipedia . | |
| 8 | Example predicates in PROLOG - continued. Test. | |
| 9 | Alpha-beta pruning. Playing against the human vs playing against the nature. Expecti-min-max. Introduction to PROLOG. | |
| 10 | PROLOG - writing simple programs. | |
| 11 | PROLOG - writing simple programs again. | |
| 12 | Rule acqusition from data - search space definition and introduction to the Rough Set methodology. | |
| 13 | Decision rules vs. decision trees. ID3 method. | |
| 14 | Metaheuristic search methods. Application of metaheuristics for inference and knowledge extraction from data. | 15 | Summary and outlook. Test. |