PPT-1 Local & Adversarial Search

CSD 15780 Graduate Artificial Intelligence Instructors Zico Kolter and Zack Rubinstein TA Vittorio Perera 2 Local search algorithms Sometimes the path to the goal is irrelevant 8queens problem jobshop scheduling. 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, . Chapter 4. Local search algorithms. Hill-climbing search. Simulated annealing search. Local beam search. Genetic algorithms. Outline. In many optimization problems, the . path. to the goal is irrelevant; the goal state itself is the . This lecture topic. Read Chapter 4.1-4.2. Next lecture topic. Read Chapter 5. (Please read lecture topic material before and after each lecture on that . topic. ). You will be expected to know. Local Search Algorithms. and. Continuous Search. Local search algorithms. In many optimization problems, the . path . to the goal is irrelevant; the goal state itself is the . solution. In such cases, we can use . local search algorithms. 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.. 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. 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 . 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. 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 . 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, . AI: Representation and Problem Solving. Local Search. Instructors: Fei Fang & Pat Virtue. Slide credits: CMU AI, http://ai.berkeley.edu. Learning Objectives. Describe and implement the following local search algorithms. Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. Learn how consumers search for local businesses and how a local search engine optimization company can help you rank higher. Contact Kapa Technologies today!