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Lecture Slides

NoTopicDateSlides
A1Organizational Matters16.02.screen
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A2What is Artificial Intelligence?16.02.screen
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A3AI Past and Present18.02.screen
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A4Rational Agents18.02.screen
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A5Environments and Problem Solving Methods02.03.screen
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B1State Spaces02.03.screen
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B2Representation of State Spaces04.03.screen
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B3Examples of State Spaces04.03.screen
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B4Data Structures for Search Algorithms09.03.screen
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B5Tree Search and Graph Search09.03.screen
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B6Breadth-first Search11.03.screen
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B7Uniform Cost Search11.03.screen
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B8Depth-first Search & Iterative Deepening16.03.screen
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B9Heuristics16.03.screen
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B10Analysis of Heuristics18.03.screen
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B11Best-first Graph Search18.03.screen
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B12Greedy Best-first Search, A*, Weighted A*23.03.screen
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B13IDA*23.03.screen
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B14Properties of A*, Part 125.03.screen
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B15Properties of A*, Part 225.03.screen
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c1Combinatorial Optimization: Introduction and Hill-Climbing30.03.screen
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c2Advanced Techniques30.03.screen
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D1Constraint Satisfaction Problems: Introduction and Examples01.04.screen
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D2Constraint Networks01.04.screen
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d3Backtracking08.04.screen
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d4Inference and Forward Checking08.04.screen
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d5Arc Consistency13.04.screen
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d6Path Consistency13.04.screen
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d7Constraint Graphs15.04.screen
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d8Decomposition Methods15.04.screen
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E1Propositional Logic: Syntax and Semantics20.04.screen
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E2Equivalence, Normal Forms, and Reasoning20.04.screen
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E3Resolution22.04.screen
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E4DPLL Algorithm22.04.screen
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E5Local Search and Outlook27.04.screen
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F1Automated Planning: Introduction27.04.screen
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F2Planning Formalisms29.04.screen
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F3Delete Relaxation29.04.screen
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F4Delete Relaxation Heuristics04.05.screen
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F5Abstraction04.05.screen
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F6Abstraction Heuristics06.05.screen
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G1Board Games: Introduction and State of the Art06.05.screen
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G2Formal Definition and Minimax Search11.05.screen
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G3Evaluation Functions11.05.screen
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G4Alpha-Beta Search13.05.screen
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G5Stochastic Games13.05.screen
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G6Monte-Carlo Tree Search Framework18.05.screen
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G7Monte-Carlo Tree Search Variants18.05.screen
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 Complete Slide Set screen
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Exercises

are provided via ADAM

Supplementary Material

Chap.Description
A2Jones and Bergen: "Large Language Models Pass the Turing Test"
A2Turing: "Computing Machinery and Intelligence"
B2bounded inc-and-square problem: explicit graph
B28-Puzzle: explicit graph, declarative representation, black box
B2Delling et al.: "Engineering Route Planning Algorithms"
B4Burns et al.: "Implementing Fast Heuristic Search Code"
B6Korf and Schultze: "Large-Scale Parallel Breadth-First Search"
B8Complexity estimation script
B9Korf: "Finding Optimal Solutions to Rubik’s Cube Using Pattern Databases"
B12Jordan Thayer: "A Brief Introduction to Search" (YouTube video)
B13Helmert et al.: "Iterative Budgeted Exponential Search"
B15Python code for the A* experiment described in Chapter B15
C1Python implementation of Hill Climbing
C2Python implementation of Hill Climbing with Stagnation
C2Python implementation of Hill Climbing with Stagnation and Randomization
C2Python implementation of Simulated Annealing
D1Numberphile video on Four Colour Problem (YouTube video)
D1McGuire et al.: "There is no 16-Clue Sudoku: Solving the Sudoku Minimum Number of Clues Problem"
D6Simonis: "Sudoku as a Constraint Problem"
E5Katebi et al.: "Empirical Study of the Anatomy of Modern SAT Solvers"
E5Heule and Scheucher: "Happy Ending: An Empty Hexagon in Every Set of 30 Points"
F2Blocks World: PDDL model
F4Keyder and Geffner: "Heuristics for Planning with Action Costs Revisited"
F6Kreft et al.: "Computing Domain Abstractions for Optimal Classical Planning with Counterexample-Guided Abstraction Refinement"
G3Giambatista Lolli: "Osservazioni teorico-pratiche sopra il Giuoco degli Scacchi ossia il Giuoco degli scacchi eposta nel suo miglior lume"
G4Alpha-Beta best-case script
G7Silver et al.: "Mastering the game of Go with deep neural networks and tree search"
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