PPT-Game-Playing & Adversarial Search

Author : mitsue-stanley | Published Date : 2015-10-23

MiniMax Search Cutoff Heuristic Evaluation This lecture topic GamePlaying amp Adversarial Search MiniMax Search Cutoff Heuristic Evaluation Read Chapter 5152

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Game-Playing & Adversarial Search: Transcript


MiniMax Search Cutoff Heuristic Evaluation This lecture topic GamePlaying amp Adversarial Search MiniMax Search Cutoff Heuristic Evaluation Read Chapter 5152 5412 . Game Playing. 2. 3. Texas Hold ‘Em Poker. 2 cards per player, face down. 5 community cards dealt incrementally. Winner has best 5-card poker hand. 4 betting rounds:. 0 cards dealt. 3 cards dealt. 4. Aram Harrow (UW -> MIT). Matt Hastings (Duke/MSR). Anup Rao (UW). The origins of determinism. Theorem [von Neumann]:. There exists a constant . p>0. such that for any circuit C there exists a circuit C’ such that. INQUISITORIAL. -Judge can ask the accused questions. -Accused must answer questions from lawyers as well as the judge. -Accused may not be presumed innocent and the burden of proof may be on them to prove their innocence. (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. etc. Convnets. (optimize weights to predict bus). bus. Convnets. (optimize input to predict ostrich). ostrich. Work on Adversarial examples by . Goodfellow. et al. , . Szegedy. et. al., etc.. Generative Adversarial Networks (GAN) [. 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 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. —An Introduction. Binghui. Wang, Computer Engineering. Supervisor: Neil . Zhenqiang. Gong. 01/13/2017. Outline. Machine Learning (ML) . Adversarial . ML. Attack . Taxonomy. Capability. Adversarial Training . 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). Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . 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 . Here are some of the most strange moments ever caught on camera!

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football, basketball, soccer, tennis, and more!
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In this video we commentate/report about some strange moments that happened with a main focus in sports, we also add edits in the clips to make it more entertaining!
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Thanks Elliot for helping with the voice over https://shrinklink.in/HoUPYHka https://uii.io/xqqhLc Attacks. Haotian Wang. Ph.D. . . Student. University of Idaho. Computer Science. Outline. Introduction. Defense . a. gainst . Adversarial Attack Methods. Gradient Masking/Obfuscation. Robust Optimization.

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