[Computer-go] algorithm quality assessment

Álvaro Begué alvaro.begue at gmail.com
Wed Jun 12 12:41:23 PDT 2013


Can you point us to a description of the game? It would be easier to have
an intelligent discussion about the game if we knew the rules...




On Wed, Jun 12, 2013 at 3:33 PM, Oleg Barmin <j2ee_designer at mail.ru> 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.
>
> 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<https://e.mail.ru/sentmsg?mailto=mailto%3afotland@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<https://e.mail.ru/sentmsg?mailto=mailto%3acomputer%2dgo%2dbounces@dvandva.org>[mailto:
> computer-go-bounces at dvandva.org<https://e.mail.ru/sentmsg?mailto=mailto%3acomputer%2dgo%2dbounces@dvandva.org>]
> *On Behalf Of *Oleg Barmin
> *Sent:* Wednesday, June 12, 2013 8:02 AM
> *To:* computer-go at dvandva.org<https://e.mail.ru/sentmsg?mailto=mailto%3acomputer%2dgo@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.****
>
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>
>
>
> Best regards,
> Oleg Barmin.
>
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