PPT-Games Outline I. Game as adversarial search

II The minimax algorithm Figuresimages 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. 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, . Statistical Relational AI. Daniel Lowd. University of Oregon. Outline. Why do we need adversarial modeling?. Because of the dream of AI. Because of current reality. Because of possible dangers. Our initial approach and results. Monte Carlo Tree Search. Minimax. search fails for games with deep trees, large branching factor, and no simple heuristics. Go: branching factor . 361 (19x19 board). Monte Carlo Tree Search. Instead . Nets. İlke Çuğu 1881739. NIPS 2014 . Ian. . Goodfellow. et al.. At a . glance. (. http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html. ). Idea. . Behind. (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). Presenters: Pooja Harekoppa, Daniel Friedman. Explaining and Harnessing Adversarial Examples. Ian J. . Goodfellow. , Jonathon . Shlens. and Christian . Szegedy. Google Inc., Mountain View, CA. Highlights . 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 . Use . adversarial learning . to suppress the effects of . domain variability. (e.g., environment, speaker, language, dialect variability) in acoustic modeling (AM).. Deficiency: domain classifier treats deep features uniformly without discrimination.. 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 . Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. Dr. Alex Vakanski. Lecture 1. Introduction to Adversarial Machine Learning. . Lecture Outline. Machine Learning (ML). Adversarial ML (AML). Adversarial examples. Attack taxonomy. Common adversarial attacks.