PPT-Game-Playing & Adversarial Search

MiniMax Search Cutoff Heuristic Evaluation This lecture topic GamePlaying amp Adversarial Search MiniMax Search Cutoff Heuristic Evaluation Read Chapter 5152 5412 . CSD 15-780: Graduate Artificial Intelligence. Instructors: . Zico. . Kolter. and Zack Rubinstein. TA: Vittorio . Perera. 2. Local search algorithms. Sometimes the path to the goal is irrelevant:. 8-queens problem, job-shop scheduling. Alpha-Beta Pruning, etc.. This lecture topic:. Game-Playing & Adversarial . Search. (Alpha-Beta . Pruning, etc.). Read Chapter . 5.3-5.5. Next lecture topic:. Constraint Satisfaction Problems (two lectures). (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. 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). 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): . 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..