Liezelle Ann Canadilla | 24 Jul 18:24 2014

Deadline Approaching for Informatics & Applications ICIA2014 - MALAYSIA

All registered papers will be included in SDIWC Digital Library, and in the proceedings of the conference.

TITLE: The Third International Conference on Informatics & Applications (ICIA2014)

EVENT VENUE: Universiti Sultan Zainal Abidin (UniSZA), Kuala Terengganu, Malaysia

CONFERENCE DATES: October 8-10, 2014


OBJECTIVE: To provide a medium for professionals, engineers, academicians, scientists, and researchers from over the world to present the result of their research activities in the field of Computer Science, Engineering and Information Technology. ICIA2014 provides opportunities for the delegates to share the knowledge, ideas, innovations and problem solving techniques. Submitted papers will be reviewed by the technical program committee of the conference.

KEYWORDS: Informatics, E-Learning, Information Ethics, Information Content Security, Anti-cyberterrorism, Real-Time Systems and many more...


SUBMISSION DATES: Open until September 26, 2014

CONTACT EMAIL: icia2014 <at>

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Peter Drake | 22 Jul 18:49 2014

Orego 8 is here!

I am proud to announce the release of version 8 of Orego:

Orego is not the strongest program out there, but it is an excellent testbed, especially for those who are new to computer Go and wish to work in Java. This latest code is pretty clean and well-organized.

I welcome your comments and suggestions.
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Michael Markefka | 21 Jul 18:33 2014

Popular Youtuber Dwyrin (~6d amateur) starting a series playing against bots on KGS

Hello everyone,

this might be of interest to some of you. Dwyrin
( -  <at> GoDwyrin) runs a popular
Youtube channel featuring high amateur dan live games, theory lessons
and game reviews. He has started a series where he plans to play
against successibely stronger bots on KGS and comments the games while
playing. The first video (
features very basic bots, so the future videos are going to be more
interesting. One of his specific interests seems to be whether the
bots would be good teaching aids and could be recommended as such.

Interested to hear what everyone thinks.

Weiji Ma | 18 Jul 21:12 2014

Postdoc position at NYU

Postdoc position: human decision-making and learning in combinatorial games

Applications are invited for a postdoctoral position in my laboratory in the Center for Neural Science and Department of Psychology at New York University ( The position is for two years, with the possibility of extension.


The long-range goal of the lab is to understand decision-making under uncertainty. The specific project is an NSF-funded one on how humans think ahead, decide, and learn in two-player, full-information games (such as chess, go, or tic-tac-toe). We will use human behavioral experiments, machine learning, eye tracking, and potentially fMRI to dissect the roles of heuristics, pruning strategy, and cognitive constraints in human play in a simplified, tractable game environment.


Applicants should have a Ph.D. in machine learning, artificial intelligence, game theory, computational neuroscience, or mathematical psychology, and be familiar with at least one other of these fields. Experience with probabilistic models is required.

To apply, please send your CV, a description of why you are interested, and the contact information of two references to me at weijima <at> Do not hesitate to email me if you have any questions. Consideration of applications will begin immediately and will end when the position is filled. Salary will be competitive and commensurate with experience and qualifications.

Wei Ji Ma, Ph.D.
Associate Professor
Center for Neural Science and Department of Psychology
New York University
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"Ingo Althöfer" | 14 Jul 12:47 2014

37x37 tournament on KGS

Hello guys,

in the "insane" section of KGS a tournament on 37x37-boards
will be played next week:

It would be interesting to see how bots perform there.
Is someone willing to let his baby participate?


PS. Some years ago Zen participated in a 21x21-tournament on KGS
and got an impressive second rank, if I remember correctly. 
Nick Wedd | 7 Jul 18:26 2014

Congratulations to Zen!

Congratulations to Zen19S, winner of the July KGS computer
Go tournament, with seven wins from seven games!

My very short report is at

As usual I will welcome any corrections and comments.


Nick Wedd
nick <at>
Martin Mueller | 3 Jul 23:02 2014

Re: IEEE Spectrum article

It is a great article overall. I would like it more if it mentions Mogo, at
least "Follow from the opponent's previous move" was actually Mogo's
invention in the famous UCT paper, not Fuego's, not to mention a lot of
Mogo's achievements on 9x9. But I really like the paragraph describing the
great idea RAVE. It might be the first introductory article (for general
people) trying to explain RAVE.

Hi Aja,

thanks for comments.

We certainly didn’t mean to short-change the MoGo team's or anybody else’s contribution. For this article there were two main points:
- try to explain as much as possible how things work in a current program
- have some kind of dramatic story for the introduction. We chose the Fuego game against Mr Chou since we are most familiar with it :)

Anything else had to be compressed into very very little space. So there was no real space for history other than the chess vs Go playing strength graph. The original version of the graph had program names attached to it, but it got simplified away.

There is quite a number of popular articles that focus on MoGo or Crazy Stone or Zen. For example, see articles on

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Greg Schmidt | 3 Jul 15:18 2014

Re: Computer-go Digest, Vol 54, Issue 4

My original example was unrealistic and on the extreme side to make a point.  However if there are nodes with
say 7/10, 12/20, and 50/100 how should they be ranked?  In some sense, the first one seems promising since
we've only searched just a few nodes, yet we are mainly seeing wins (granted, the last one expresses more
confidence).  To me, this would seem like an argument that favors win rate over win count (and we don't
always have the benefit of extending the search for these "low confidence nodes" until the law of large
numbers sorts things out).

Perhaps there is an argument that the UCB formula "won't generally let this happen" since it takes into
consideration both win rate and tries to increase confidence by promoting the visit of nodes with low
visit counts.  Still, I can envision cases where time is running out, and UCT has just recently discovered a
promising new branch.

If the choice were 1/2 vs. 50/100, the second expresses more confidence yet one could argue we should search
the first move more (given that time is available to do so) to find out if it is, in fact, better than .5 (since
being low confidence, it has a reasonable chance of being better than the 2nd).  So the general question
becomes how to effectively trade off win rate with win rate confidence?

FWIW, the sample code at

Does something completely different, it selects the root node which has the most number of visits:

"return sorted(rootnode.childNodes, key = lambda c: c.visits)[-1].move # return the move that was most visited"

 On Thu, Jul 03, 2014 at 10:57:17AM +0200, Stefan Kaitschick
 > On Thu, Jul 3, 2014 at 9:00 AM, Darren Cook <darren <at>>
 > >
 > > If you had a choice between a 1% 65,000-wins move
 and a 70% 7-wins move,
 > > MCTS will keep exploring the 70% move, until it
 either reaches 65,001
 > > wins, and can be chosen, or the winning percentage
 comes down to 1% also.
 > >
 > > BTW, that implies it would be very difficult to
 ever reach the situation
 > > you describe, as 1% win rate moves wouldn't be
 given 650,000 trials
 > > (unless all other moves on the board are equally
 bad, i.e. the game is
 > > clearly lost).
 > >
 > What I dont understand, is why the variation that's
 trying to catch up has
 > to absolutely overtake the leader.
 > Shouldn't there be a substantial bonus for a late high
 success rate?

 The way this bonus is often implemented is that if a
 disparity between
 the move with the highest winrate and the move with the
 highest number
 of simulations is different, the program spends some extra
 searching to give a chance to resolve this.

   (Conversely, if some move has been simulated so much
 more that no
 other move can overtake it in the number of simulations in
 the alloted
 time anymore, the search can be stopped early.)

 P.S.: No, I don't like Japanese byoyomi for Pachi. ;-)

     Petr Baudis
     Life is short, the craft long,
 opportunity fleeting, experiment
     treacherous, judgment difficult.  --


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 End of Computer-go Digest, Vol 54, Issue 4
Greg Schmidt | 3 Jul 04:13 2014

Re: ieee aticle about computer go by Jonathan Schaeffer

"Determining the best move is tricky, however. The most natural approach would be to pick the move with the
highest probability of leading to a win. But this is usually too risky. For example, a move with 7 wins out of
10 trials may have the highest odds of winning (70 percent), but because this number comes from only 10
trials, the uncertainty is high. A move with 65,000 wins out of 100,000 trials (65 percent) is a safer bet.
This suggests a different strategy:

Choose the move with the largest number of wins. And this is indeed the standard approach."

Really? Changing the example, what if the 65,000 wins were out of 650,000? (1% win rate vs. 70% win rate),
then does it always make sense to choose the path with the most number of moves?
Martin Mueller | 2 Jul 19:15 2014

Re: ieee article about computer go

We had no control over the title page art. The IEEE art dept gave the following explanation for their work:

"The opening art for the story is conceptual and illustrative. It is not meant to show a game in progress, but
rather the idea of AI for Go. We took the game elements (board, stones) and used them to create a brain shape
and evoke digital pixels.”

Hope that helps :)

Xavier Combelle | 1 Jul 15:43 2014

ieee aticle about computer go by Jonathan Schaeffer

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