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Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Lecture 9 of 42 William H. Hsu Department.

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Presentation on theme: "Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Lecture 9 of 42 William H. Hsu Department."— Presentation transcript:

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2 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Lecture 9 of 42 William H. Hsu Department of Computing and Information Sciences, KSU KSOL course page: http://snipurl.com/v9v3http://snipurl.com/v9v3 Course web site: http://www.kddresearch.org/Courses/CIS730http://www.kddresearch.org/Courses/CIS730 Instructor home page: http://www.cis.ksu.edu/~bhsuhttp://www.cis.ksu.edu/~bhsu Reading for Next Class: Section 7.1 – 7.4, p. 194 - 210, Russell & Norvig 2 nd edition Outside reference: University of Alberta GAMES page – http://www.cs.ualberta.ca/~games/http://www.cs.ualberta.ca/~games/ Game Tree Search: Minimax and Alpha-Beta (α-β) Pruning

3 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Lecture Outline Reading for Next Class: 7.1 – 7.4 (p. 194 – 210), R&N 2 e Last Class, 5.4-5.5, p. 151-158; Games Intro, 6.1-6.3, p. 161-174  Third CSP algorithm: constraint propagation by arc consistency (AC-3)  “One-step” vs. “all-steps” lookahead Today: Game Tree Search  Rudiments of game theory  Minimax with alpha-beta (α-β) pruning  Perfect information vs. imperfect information Need for Expectiminimax  Games of chance: dealing with nondeterminism  Imperfect information Game Analysis  Quiescence  Horizon effect  “Averaging over clairvoyance” and when/why it fails Next Class: From Search to Knowledge Representation

4 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Types of Games: Review Based on slide © 2004 S. Russell & P. Norvig. Reused with permission. Chess http://tr.im/zdTD Checkers http://tr.im/zdTW Go http://tr.im/zdVn Reversi (Othello) http://tr.im/zdVr Battleship © Milton Bradley http://tr.im/zdWK Tic-Tac-Toe http://tr.im/zdXB Backgammon http://tr.im/ze1P Monopoly © Parker Brothers http://tr.im/ze2F Contract Bridge http://tr.im/ze5D Poker (Texas Hold ’Em) http://tr.im/ze7W Scrabble © Hasbro http://tr.im/ze90

5 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence ≥ 3 3 3 MAX MIN MAX 128 ≤ 2 2145 ≤ 14 2 ≤52 Figure 6.5 p. 168 R&N 2 e ,  here? What are ,  values here? Alpha-Beta ( α-β ) Pruning – Example: Review Adapted from slides © 2004 S. Russell & P. Norvig. Reused with permission.

6 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence © 2004 S. Russell & P. Norvig. Reused with permission. Minimax with α-β Pruning – Algorithm: Review

7 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence © 2004 S. Russell & P. Norvig. Reused with permission. Why Is It Called α-β ? Figure 6.6 p. 169 R&N 2 e

8 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Adapted from slide © 2004 S. Russell & P. Norvig. Reused with permission. Can We Do Better? Idea: Adapt Resource-Bounded Heuristic Search Techniques  Depth-limited  Iterative deepening  Memory-bounded Depth Limit Rationale: Review

9 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Resource Limits and Limited-Ply Search Based on slide © 2004 S. Russell & P. Norvig. Reused with permission.

10 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Static Evaluation Functions: Review © 2004 S. Russell & P. Norvig. Reused with permission. Figure 6.8 p. 173 R&N 2 e

11 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Digression: Exact Values Don’t Matter © 2004 S. Russell & P. Norvig. Reused with permission.

12 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Issues  Quiescence  Play has “settled down”  Evaluation function unlikely to exhibit wild swings in value in near future  Horizon effect  “Stalling for time”  Postpones inevitable win or damaging move by opponent  See: Figure 6.9, p. 175 R&N 2 e Solutions?  Quiescence search: expand non-quiescent positions further  No general solution to horizon problem at present Quiescence and the Horizon Effect Adapted from slides © 2004 S. Russell & P. Norvig. Reused with permission.

13 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Deterministic Games in Practice © 2004 S. Russell & P. Norvig. Reused with permission.

14 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence © 2004 S. Russell & P. Norvig. Reused with permission. Nondeterministic Games: Backgammon

15 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence © 2004 S. Russell & P. Norvig. Reused with permission. Nondeterministic Games in General

16 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence © 2004 S. Russell & P. Norvig. Reused with permission. Expectiminimax: Algorithm for Nondeterministic Games

17 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Nondeterministic Games in Practice © 2004 S. Russell & P. Norvig. Reused with permission.

18 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Digression: Exact Values DO Matter © 2004 S. Russell & P. Norvig. Reused with permission. Figure 6.12 p. 178 R&N 2 e

19 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Games of Imperfect Information [1]: Solution Approach © 2004 S. Russell & P. Norvig. Reused with permission.

20 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Adapted from slides © 2004 S. Russell & P. Norvig. Reused with permission. Games of Imperfect Information [2]: Example Similar to example on p. 179 R&N 2 e

21 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Adapted from slides © 2004 S. Russell & P. Norvig. Reused with permission. Commonsense Example [1]: Statement Day 1 Day 2 Day 3

22 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence © 2004 S. Russell & P. Norvig. Reused with permission. Commonsense Example [2]: Proper Analysis

23 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence © 2004 S. Russell & P. Norvig. Reused with permission. Games: Summary

24 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Terminology Game Tree Search  Zero-sum games  2-player vs. n-player Minimax Algorithm: Alternates between MAX and MIN Players Alpha-Beta Pruning (α-β Pruning)  α: best value to MAX found so far off current path (v worse than α  prune)  β: best value to MIN found so far off current path Resource-Bounded Minimax  Static evaluation function  Limited-ply search (compare: depth-limited search aka DLS)  Iterative deepening search (compare: ID-DFS, IDA*) Expectiminimax  Based on expectation  Games with chance  Games with imperfect information

25 Computing & Information Sciences Kansas State University Lecture 9 of 42 CIS 530 / 730 Artificial Intelligence Summary Points Game Theory Continued  Game tree representation  Perfect play: Minimax algorithm, speedup with alpha-beta (α-β) pruning  Resource-bounded Minimax: static evaluation functions, iterative deepening  Emphasis: two-player (with exceptions), zero-sum, perfect info Alpha-Beta Pruning (α-β Pruning) Resource-Bounded Minimax  Need for static evaluation (compare: heuristics)  Limited-ply search (compare: depth-limited search aka DLS)  Iterative deepening search (compare: ID-DFS, IDA*) Expectiminimax  Based on expectation  Games with chance  Games with imperfect information Significance of Games to AI  Understanding representation, reasoning, and learning  Finding out what approximations, refinements, abstractions work

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