Fwd: Fwd: IEEE CIS webinar: The Magic of Monte Carlo Tree Search - Sept. 29 2017

[Saullo Haniell]

---------- Forwarded message ----------
From: 陈欢欢 <hc...@ustc.edu.cn>
Date: Thu, Sep 21, 2017 at 10:15 AM
Subject: Fwd: Fwd: IEEE CIS webinar: The Magic of Monte Carlo Tree Search - Sept. 29 2017
To: CIS-WE...@listserv.ieee.org, "Minku, Leandro L. (Dr.)" <leandr...@leicester.ac.uk>, "Pau-Choo (Julia) Chung" <pcc...@ee.ncku.edu.tw>, ckt...@cs.ccu.edu.tw, jwh...@mail.ncku.edu.tw

Sorry to forget to include the information to register for the webinar.

Please register for The Magic of Monte Carlo Tree Search - Sep 29, 2017 on 4:00 PM BST at:

https://attendee.gotowebinar.com/register/5997375619679435011



-------- 转发的消息 --------

主题:	Fwd: IEEE CIS webinar: The Magic of Monte Carlo Tree Search - Sept. 29 2017
日期:	Thu, 21 Sep 2017 19:52:50 +0800
发件人:	陈欢欢 <hc...@ustc.edu.cn>
收件人:	CIS-WE...@LISTSERV.IEEE.ORG
抄送:	jiali...@qmul.ac.uk, 陈欢欢 <hc...@ustc.edu.cn>

Dear All

Thanks for Jialin Liu. A webinar will be given by Dr. Mark Winands, Associate Professor, Department of Data Science & Knowledge Engineering, Maastricht University.

The abstract and bio of the speaker can be found below.

We will organise an onsite event at Queen Mary University of London (UK) in case some of the audience want to and are able to attend.

Title: The Magic of Monte Carlo Tree Search

Date and Time: 4pm (BST), Friday, Sept. 29 2017

Abstract

Monte-Carlo Tree Search (MCTS) has caused a revolution in computer game-playing the last few years. The most well-known example is the game of Go. MCTS is a best-first search technique that gradually builds up a search tree, guided by  Monte-Carlo  simulations. In contrast to many classic search techniques, MCTS does not require a heuristic evaluation function that assesses the current board position. In this talk I will discuss its background, basic mechanism, and standard enhancements that have improved the technique considerably. Successful applications of the technique in several domains will be mentioned.

Bio

Mark Winands received a Ph.D. degree in Artificial Intelligence from the Department of Computer Science, Maastricht University, Maastricht, The Netherlands, in 2004. Currently, he is an Associate Professor at the Department of Data Science & Knowledge Engineering, Maastricht University. His research interests include heuristic search, machine learning and games. He has written more than eighty scientific publications on Games & AI. Mark serves as an editor-in-chief of the ICGA Journal, associate editor of IEEE Transactions on Computational Intelligence and AI in Games, editor of Game & Puzzle Design. He is a member of the Games Technical Committee (GTC) | IEEE Computational Intelligence Society, and member of working group 14.4 – Entertainment Games | IFIP TC14 on Entertainment Computing.

Venue:

Room: Bancroft road teaching room 3.02, Peter Landin Building, EECS, QMUL

Address: 10, Godward Square, Queen Mary University of London, Mile End Rd, London E1 4FZ

Mile End campus map: https://drive.google.com/file/d/0B7osFXI6r9pHYWdRQ051SzlKLVE/view
 (Peter Landin Building is the number 6)

Thank you!

Best wishes,
Huanhuan