PPT-Games and adversarial search

Author : tatiana-dople | Published Date : 2017-03-16

Chapter 5 World Champion chess player Garry Kasparov is defeated by IBMs Deep Blue chessplaying computer in a sixgame match in May 1997 link Telegraph Group

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Games and adversarial search: Transcript


Chapter 5 World Champion chess player Garry Kasparov is defeated by IBMs Deep Blue chessplaying computer in a sixgame match in May 1997 link Telegraph Group Unlimited 1997. MiniMax. , Search Cut-off, Heuristic Evaluation. This lecture topic:. Game-Playing & Adversarial Search . (. MiniMax. , Search Cut-off, . Heuristic . Evaluation). Read Chapter 5.1-5.2. , 5.4.1-2, . Search. (game playing search). We have experience in search where we assume that we are the only intelligent . entity and . we have explicit control over the “world”.. Let us . consider what happens when we relax those assumptions.. Chapter 6. Section 1 – 4. Outline. Optimal decisions. α-β pruning. Imperfect, real-time decisions. Games vs. search problems. "Unpredictable" opponent . . specifying a move for every possible opponent . (Chapter 5). World Champion chess player Garry Kasparov . is . defeated by IBM’s Deep Blue chess-playing computer in a . six-game . match in May, . 1997. (. link. ). © Telegraph Group . Unlimited 1997. (Chapter 5). World Champion chess player Garry Kasparov . is . defeated by IBM’s Deep Blue chess-playing computer in a . six-game . match in May, . 1997. (. link. ). © Telegraph Group . Unlimited 1997. We have experience in search where we assume that we are the only intelligent entity and we have explicit control over the “world”.. Let us consider what happens when we relax those assumptions. We have an . CS344 Seminar Presentation. 1. Group 4. Team Members. (In the lexicographic order of roll number):. Adhip. Agarwal (07005009). Raman Sharma (07005010). Gaurav Malpani (07005011). Sumit. . Somani. (07005012). earch. Why study games?. Games are a traditional hallmark of intelligence. Games are easy to formalize. Games can be a good model of real-world competitive activities. Military confrontations, negotiation, auctions, etc.. MiniMax. , Search Cut-off, Heuristic Evaluation. This lecture topic:. Game-Playing & Adversarial Search . (. MiniMax. , Search Cut-off, . Heuristic . Evaluation). Read Chapter 5.1-5.2. , 5.4.1-2, . earch. Why study games?. Games can be a good model of many competitive activities. Military confrontations, negotiation, auctions, …. Games are a traditional hallmark of intelligence. Contrarian viewpoint (textbook): . Games and adversarial s earch Why study games? Games can be a good model of many competitive activities Military confrontations, negotiation, auctions, … Games are a traditional hallmark of intelligence AIMA . Chapter. 5.1 – 5.5. AI vs. Human Players: the State of the Art. 4. To Be Updated next year!. Deterministic. Games . in. Practice. Checkers: . Chinook . ended 40-year-reign of human world champion Marion Tinsley in 1994. Used a . Mini-max, Cutting Off Search. CS. 171. , . Summer 1. . Quarter, 2019. Introduction to Artificial Intelligence. Prof. . Richard Lathrop. Read Beforehand:. R&N 5.1, 5.2, 5.4. Outline. Computer . programs that play 2-player . II. The minimax algorithm. * Figures/images are from the . textbook site . (or by the instructor) . Otherwise, the source is specifically cited . . unless citation . would make little sense due to the triviality of generating such an image..

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