[Computer-go] algorithm quality assessment

Jason House jason.james.house at gmail.com
Wed Jun 12 17:16:41 PDT 2013

```The algorithm I put was based on the first link which did not have an open face variant.

My interpretation was as follows:
After splitting your 13 cards into 3 hands, the top hands are compared to each other. The best top hand gets a point and the lowest loses a point. The same goes for the middle and bottom hands. That yields a score between -3 and +3.

If you dump everything into one best hand, you're easily defeated by opponents who try to keep their hands at comparable strength... You would earn one point for your best hand and lose one point from each of the other two hands (net result = -1 point). There is some kind of balancing that must be done to get the best score.

Sent from my iPhone

On Jun 12, 2013, at 6:08 PM, Don Dailey <dailey.don at gmail.com> wrote:

> I don't quite see the point.   The goal is to find the best possible hand YOU can make with your 13 cards and there is no betting,   so I see no point in using Monte Carlo here.
>
> What am I missing?
>
> Is it whether to sacrifice one of the 3 hands to strengthen the other 2?  Or in the case of a really bad hand to at least make 1 really strong hand?
>
> Don
>
>
> On Wed, Jun 12, 2013 at 6:03 PM, Jason House <jason.james.house at gmail.com> wrote:
>> For a particular breakdown into 3 hands, it should be possible to do a monte carlo simulation by randomly distribute the remaining cards to the other players and then randomly separating each player's cards into 3 hands. A node in the search tree would be scored as the average result of many simulations.
>>
>> I can think of a few ways to build a search tree. If you have experience in the game and know a few general strategies, they will likely be very handy for achieving enough strength to evaluate the approach. The search tree should be able to give feedback on which strategy is best. The same strategies may also help improve the random opponents, but that might require more care. It's easy to introduce bias.
>>
>> Sent from my iPhone
>>
>> On Jun 12, 2013, at 4:06 PM, Oleg Barmin <j2ee_designer at mail.ru> wrote:
>>
>>> Sure. It's open chinese poker: http://www.pokerlistings.com/poker-rules-chinese-poker
>>>
>>>
>>> Среда, 12 июня 2013, 20:57 +01:00 от Nick Wedd <nick at maproom.co.uk>:
>>> 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.
>>> >
>>> >
>>> > _______________________________________________
>>> > Computer-go mailing list
>>> > Computer-go at dvandva.org
>>> > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
>>> >
>>>
>>>
>>> --
>>> Nick Wedd
>>> nick at maproom.co.uk
>>> _______________________________________________
>>> Computer-go mailing list
>>> Computer-go at dvandva.org
>>> http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
>>>
>>>
>>> Best regards,
>>> Oleg Barmin.
>>> _______________________________________________
>>> Computer-go mailing list
>>> Computer-go at dvandva.org
>>> http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
>>
>> _______________________________________________
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