'Monte carlo tree search' diaporamas de présentation

CS534 Spring 2019 Adversarial Search, Game Playing

CS534 Spring 2019 Adversarial Search, Game Playing Showcase by: Varun Bhat, Ruofan Hu, Jiayi Li, Justin Seeley, and Matthew Szpunar. Showcasing work by:

558 views • 42 slides

Artificial Intelligence

Artificial Intelligence. Prabhas Chongstitvatana Chulalongkorn University p rabhas.c@chula.ac.th. AI : Past, Present, Future. Why AI is popular today?. Performance in "human task" Tagging Faces Search Music Personal assistance Play Go. Tiny computer for 5 dollars. Past.

265 views • 26 slides

CS440/ECE448 Midterm Review Fall 2016

CS440/ECE448 Midterm Review Fall 2016. Things to know. Review of topics Review exam. Topics. Intro to AI Search Constraint Satisfaction Problems Games Game Theory Planning. 1. Intro to AI. AI = human thought, human action, rational thought, rational action?

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A Brief History o f Computer Go

261 views • 19 slides

Game Intelligence: The Future

368 views • 18 slides

Amsterdam, The Netherlands July 06-10, 2013

Rolling Horizon Evolution versus Tree Search for Navigation i n Single-Player Real-Time Games. Diego Perez, Spyridon Samothrakis , Simon M. Lucas and Philipp Rohlfshagen. Games Intelligence Group University of Essex, UK. DETA2, Evolution in Music and Games. Amsterdam, The Netherlands

306 views • 16 slides

Each arm has unknown random sequences, unknown expected value

65. 65. 62. 65. 90. 62. 83. 65. 35. 90. 49. 62. 30. 83. 80. Max node. Min node. UCB value. Expected value. Bias. Example of a finished game on 9x9 board. (Official games use 19x19 board). Imai Laboratory Introduction. Monte-Carlo Tree Search.

134 views • 1 slides