University of Basel

Department of
Mathematics and Computer Science

""

Important Links

Lecture Slides

NoTopicDateSlides
A1Organizational Matters17.02.

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A2What is Artificial Intelligence?17.02.screen
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A3AI Past and Present19.02.screen
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A4Rational Agents19.02.screen
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A5Introduction: Environments and Problem Solving Methods24.02.

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B1State-Space Search: State Spaces24.02.screen
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B2State-Space Search: Representation of State Spaces26.02.screen
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B3State-Space Search: Examples of State Spaces26.02.screen
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B4State-Space Search: Data Structures for Search Algorithms03.03.screen
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B5State-Space Search: Tree Search and Graph Search03.03.screen
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B6State-Space Search: Breadth-first Search05.03.screen
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B7State-Space Search: Uniform Cost Search05.03.screen
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B8State-Space Search: Depth-first Search & Iterative Deepening17.03.screen
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B9State-Space Search: Heuristics17.03.screen
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B10State-Space Search: Analysis of Heuristics19.03.screen
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B11State-Space Search: Best-first Graph Search19.03.screen
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B12State-Space Search: Greedy BFS, A*, Weighted A∗26.03.screen
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B13State-Space Search: IDA∗26.03.screen
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B14State-Space Search: Properties of A∗, Part I31.03.screen
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B15State-Space Search: Properties of A∗, Part II31.03.screen
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C1Combinatorial Optimization: Introduction and Hill-Climbing02.04.screen
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C2Combinatorial Optimization: Advanced Techniques02.04.screen
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D1Constraint Satisfaction Problems: Introduction and Examples07.04.screen
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D2Constraint Satisfaction Problems: Constraint Networks07.04.screen
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D3Constraint Satisfaction Problems: Backtracking09.04.screen
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D4Constraint Satisfaction Problems: Arc Consistency09.04.screen
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D5Constraint Satisfaction Problems: Path Consistency14.04.screen
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D6Constraint Satisfaction Problems: Constraint Graphs14.04.screen
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D7Constraint Satisfaction Problems: Decomposition Methods16.04.screen
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E1Propositional Logic: Syntax and Semantics16.04.screen
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E2Propositional Logic: Equivalence and Normal Forms23.04.screen
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E3Propositional Logic: Reasoning and Resolution23.04.screen
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E4Propositional Logic: DPLL Algorithm28.04.screen
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E5Propositional Logic: Local Search and Outlook28.04.screen
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F1Automated Planning: Introduction30.04.screen
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F2Automated Planning: Planning Formalisms30.04.screen
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F3Automated Planning: Delete Relaxation05.05.screen
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F4Automated Planning: Delete Relaxation Heuristics05.05.screen
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F5Automated Planning: Abstraction07.05.screen
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F6Automated Planning: Abstraction Heuristics07.05.screen
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G1Board Games: Introduction and State of the Art14.05.screen
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G2Board Games: Minimax Search and Evaluation Functions14.05.screen
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G3Board Games: Alpha-Beta Search19.05.screen
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G4Board Games: Stochastic Games19.05.screen
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G5Board Games: Monte-Carlo Tree Search Framework21.05.screen
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G6Board Games: Monte-Carlo Tree Search Variants21.05.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"
A3Turing: "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"
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"
D5Simonis: "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"
G2Giambatista Lolli: "Osservazioni teorico-pratiche sopra il Giuoco degli Scacchi ossia il Giuoco degli scacchi eposta nel suo miglior lume"
G3Alpha-Beta best-case script
G6David Silver at al.: "Mastering the game of Go with deep neural networks and tree search"
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