[Computer-go] algorithm quality assessment

Nick Wedd nick at maproom.co.uk
Wed Jun 12 12:57:03 PDT 2013


On 12/06/2013 20:33, Oleg Barmin wrote:
>  > For quality assessment, play many games against one or more reference
> opponents.
> It's difficult to assament algorithm with a game against humans. The
> game is young and there are no recognized masters at the moment. So it's
> very hard to find human-opponent with a really good game skills.
>
>  >  With card games you can get some serious intransitivity,  rocks,
> paper, scissors type of stuff.
> The aim of this game is to max your scores. Each turn you need to select
> one of three choices. Each choice has an expectation value of your
> scores. Optimal strategy here is to select a choice with max expectation
> value. But it will take a years to calculate an expectation value at the
> start of the game. So the game has no such intransitivity as rocks,
> paper, scissors.
> At the last turns we can make a complete choice enumeration and
> calculate an exact scores expectation value ( does go algorithms use the
> same technique? ) . It's not the way for the first half of the game. But
> the first half is more important.

Can you give a link to the rules of this game?  Or even just tell us its 
name?

Nick

>
> Oleg
>
>
> Среда, 12 июня 2013, 14:24 -04:00 от Don Dailey <dailey.don at gmail.com>:
>
>
>
>     On Wed, Jun 12, 2013 at 11:30 AM, David Fotland
>     <fotland at smart-games.com
>     <sentmsg?mailto=mailto%3afotland at smart%2dgames.com>> wrote:
>
>         For quality assessment, play many games against one or more
>         reference opponents.
>
>
>     Especially for a game that is not a game of perfect information such
>     as go or chess.   With card games you can get some serious
>     intransitivity,  rocks, paper, scissors type of stuff.
>
>     Don
>
>
>         ____
>
>         __ __
>
>         David____
>
>         __ __
>
>         *From:*computer-go-bounces at dvandva.org
>         <sentmsg?mailto=mailto%3acomputer%2dgo%2dbounces at dvandva.org>
>         [mailto:computer-go-bounces at dvandva.org
>         <sentmsg?mailto=mailto%3acomputer%2dgo%2dbounces at dvandva.org>]
>         *On Behalf Of *Oleg Barmin
>         *Sent:* Wednesday, June 12, 2013 8:02 AM
>         *To:* computer-go at dvandva.org
>         <sentmsg?mailto=mailto%3acomputer%2dgo at dvandva.org>
>         *Subject:* [Computer-go] algorithm quality assessment____
>
>         __ __
>
>         Hi, everybody,____
>
>         I am working at the development of a cards game algorithm using
>         MCTS. Technically, the game model is expect minmax tree search,
>         where direct search takes up too much time, that is why I
>         decided to use MCTS.____
>
>         The issue of using MCST, like any other approximation algorithm
>         is its quality assessment. I am developing an algorithm for a
>         game where no recognized masters exist. How do you think, guys,
>         if for instance Go (or Amazons) provided no way to assess an
>         algorithm playing with professional gamers (or other programs),
>         how would you assets its quality?____
>
>         My second question: I have not yet learned Go in and out,
>         however in my opinion, any search of a next step should identify
>         a number of options with similar or even the same assessment.
>         How do you resolve this issue?____
>
>
>         Best regards,
>         Oleg Barmin.____
>
>
>         _______________________________________________
>         Computer-go mailing list
>         Computer-go at dvandva.org
>         <sentmsg?mailto=mailto%3aComputer%2dgo at dvandva.org>
>         http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
>
>
>
>
> Best regards,
> Oleg Barmin.
>
>
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>


-- 
Nick Wedd
nick at maproom.co.uk



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