## Lecture Slides

NoTopicDateSlides
0Organizational Matters17.2.screen
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1Introduction17.2.screen
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2Introduction: AI Past and Present19.2.screen
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3Introduction: Rational Agents19.2.screen
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4Introduction: Environments and Problem Solving Methods24.2.screen
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5State-Space Search: State Spaces24.2.screen
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6State-Space Search: Representation of State Spaces26.2.screen
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7State-Space Search: Examples of State Spaces26.2.screen
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8State-Space Search: Data Structures for Search Algorithms9.3.screen
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9State-Space Search: Tree Search and Graph Search9.3.screen
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11State-Space Search: Uniform Cost Search16.3.screen
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12State-Space Search: Depth-first Search & Iterative
Deepening
18.3.screen
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13State-Space Search: Heuristics18.3.screen
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14State-Space Search: Analysis of Heuristics23.3.screen
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15State-Space Search: Best-first Graph Search23.3.screen
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16State-Space Search: Greedy BFS, A*, Weighted A*25.3.screen
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17State-Space Search: IDA*25.3.screen
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18State-Space Search: Properties of A*, Part I30.3.screen
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19State-Space Search: Properties of A*, Part II30.3.screen
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20Combinatorial Optimization: Introduction and Hill-Climbing1.4.screen
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22Constraint Satisfaction Problems: Introduction and Examples6.4.screen
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23Constraint Satisfaction Problems: Constraint Networks6.4.screen
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24Constraint Satisfaction Problems: Backtracking8.4.screen
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25Constraint Satisfaction Problems: Arc Consistency8.4.screen
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26Constraint Satisfaction Problems: Path Consistency13.4.screen
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27Constraint Satisfaction Problems: Constraint Graphs15.4.screen
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28Constraint Satisfaction Problems: Decomposition Methods15.4.screen
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29Propositional Logic: Basics20.4.screen
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30Propositional Logic: Reasoning and Resolution20.4.screen
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31Propositional Logic: DPLL Algorithm22.4.screen
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32Propositional Logic: Local Search and Outlook22.4.screen
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33Automated Planning: Introduction27.4.screen
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34Automated Planning: Planning Formalisms27.4.screen
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35Automated Planning: Delete Relaxation29.4.screen
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36Automated Planning: Delete Relaxation Heuristics29.4.screen
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37Automated Planning: Abstraction4.5.screen
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38Automated Planning: Landmarks6.5screen
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39Automated Planning: Landmark Heuristics6.5screen
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40Board Games: Introduction and State of the Art11.5screen
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41Board Games: Minimax Search and Evaluation Functions11.5screen
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42Board Games: Alpha-Beta Search13.5screen
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43Monte-Carlo Tree Search: Introduction18.5screen
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45AlphaGo and Outlook20.5screen
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## Supplementary Material

Chap.Description
1Turing: "Computation Machinery and Intelligence"
2Bowling et al.: "Heads-up Limit Holdâ€™em Poker is Solved"
2DARPA challenge videos: 1, 2
2Silver et al.: "Mastering the game of Go with deep neural networks and tree search" (*)
68-Puzzle: explicit graph, declarative representation, black box
6Delling et al.: "Engineering Route Planning Algorithms"
8Burns et al.: "Implementing Fast Heuristic Search Code"
10Korf and Schultze: "Large-Scale Parallel Breadth-First Search"
12Complexity estimation script
17Korf: "Depth-First Iterative Deepening: An Optimal Admissible Tree Search" (*)
17Helmert et al.: "Iterative Budgeted Exponential Search"
19Dechter and Pearl: "Generalized Best-First Search Strategies and the Optimality of A*" (*)
20Hill climbing script
21Hill climbing with stagnation script
21Hill climbing with stagnation and random file script
22McGuire et al.: "There is no 16-Clue Sudoku"
22Numberphile video on Four Colour Problem
26Simonis: "Sudoku as a Constraint Problem"
31Davis, Logemann and Loveland: "A Machine Program for Theorem-Proving" (*)
32Katebi et al.: "Empirical Study of the Anatomy of Modern SAT Solvers"
32Bayless et al.: "SAT Modulo Monotonic Theories"
34Blocksworld PDDL model
36Keyder and Geffner: "Heuristics for Planning with Action Costs Revisited"
40Shannon: "Programming a Computer for Playing Chess" (*)
42Schaeffer et al.: "Checkers Is Solved" (*)
45Silver et al.: "Mastering the game of Go with deep neural networks and tree search" (*)

(*) Please get in touch with us if you are interested in this material. For copyright reasons, we are not allowed to make it available online.