Important Links
Lecture Slides
No | Topic | Date | Slides |
---|---|---|---|
A1 | Organizational Matters | 26.02. | |
A2 | What is Artificial Intelligence? | 26.02. | |
A3 | AI Past and Present | 28.02. | |
A4 | Rational Agents | 28.02. | |
A5 | Introduction: Environments and Problem Solving Methods | 04.03. | |
B1 | State-Space Search: State Spaces | 04.03. | |
B2 | State-Space Search: Representation of State Spaces | 06.03. | |
B3 | State-Space Search: Examples of State Spaces | 06.03. | |
B4 | State-Space Search: Data Structures for Search Algorithms | 11.03. | |
B5 | State-Space Search: Tree Search and Graph Search | 11.03. | |
B6 | State-Space Search: Breadth-first Search | 13.03. | |
B7 | State-Space Search: Uniform Cost Search | 13.03. | |
B8 | State-Space Search: Depth-first Search & Iterative Deepening | 18.03. | |
B9 | State-Space Search: Heuristics | 18.03. | |
B10 | State-Space Search: Analysis of Heuristics | 20.03. | |
B11 | State-Space Search: Best-first Graph Search | 20.03. | |
B12 | State-Space Search: Greedy BFS, A*, Weighted A∗ | 25.03. | |
B13 | State-Space Search: IDA∗ | 25.03. | |
B14 | State-Space Search: Properties of A∗, Part I | 27.03. | |
B15 | State-Space Search: Properties of A∗, Part II | 27.03. | |
C1 | Combinatorial Optimization: Introduction and Hill-Climbing | 03.04. | |
C2 | Combinatorial Optimization: Advanced Techniques | 03.04. | |
D1 | Constraint Satisfaction Problems: Introduction and Examples | 08.04. | |
D2 | Constraint Satisfaction Problems: Constraint Networks | 08.04. | |
D3 | Constraint Satisfaction Problems: Backtracking | 10.04. | |
D4 | Constraint Satisfaction Problems: Arc Consistency | 10.04. | |
D5 | Constraint Satisfaction Problems: Path Consistency | 15.04. | |
D6 | Constraint Satisfaction Problems: Constraint Graphs | 15.04. | |
D7 | Constraint Satisfaction Problems: Decomposition Methods | 17.04. | |
E1 | Propositional Logic: Syntax and Semantics | 17.04. | |
E2 | Propositional Logic: Equivalence and Normal Forms | 22.04. | |
E3 | Propositional Logic: Reasoning and Resolution | 22.04. | |
E4 | Propositional Logic: DPLL Algorithm | 24.04. | |
E5 | Propositional Logic: Local Search and Outlook | 24.04. | |
F1 | Automated Planning: Introduction | 29.04. | |
F2 | Automated Planning: Planning Formalisms | 29.04. | |
F3 | Automated Planning: Delete Relaxation | 06.05. | |
F4 | Automated Planning: Delete Relaxation Heuristics | 06.05. | |
F5 | Automated Planning: Abstraction | 08.05. | |
F6 | Automated Planning: Abstraction Heuristics | 08.05. | |
G1 | Board Games: Introduction and State of the Art | 13.05. | |
G2 | Board Games: Minimax Search and Evaluation Functions | 13.05. | |
G3 | Board Games: Alpha-Beta Search | 15.05. | |
G4 | Board Games: Stochastic Games | 15.05. | |
G5 | Board Games: Monte-Carlo Tree Search Framework | 22.05. | |
G6 | Board Games: Monte-Carlo Tree Search Variants | 22.05. | |
Complete Slide Set |
Exercises
are provided via ADAM
Supplementary Material
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