instead of randomsing a jigo maybe it is an idea to count all outcomes double

- count a win as 2 W
- count a loss as 2 L
this gives the option to
- count a jigo as 1 W  and 1 L



On Thu, Mar 31, 2011 at 6:06 PM,  <[email protected]> wrote:
> I used to have "solved"-flags in the tree, but the implementation was so
> messy and caused bugs in interactions with other messy stuff so I removed it
> and was happy.
>
> I guess introducing some other kind of exploration might be better. Or maybe
> value jigo as just a little better than losing rather than as 50% but then
> my simple solution currently is not good enough I would have to change
> datatypes and rewrite a lot.
>
> -Magnus
>
> Quoting René van de Veerdonk <[email protected]>:
>
>> Magnus,
>>
>> If you reach a final node, you can mark it as "solved", and never explored
>> it again. Shouldn't that solve your symptoms? It appears from your
>> description that you do not have such a feature in your tree-search.
>>
>> René
>>
>> On Thu, Mar 31, 2011 at 8:42 AM, <[email protected]> wrote:
>>
>>> I did not want to spend a lot of time changing Valkyria completely for
>>> integer komi so I made a very simple hack to cope with the fact that I am
>>> using integers and booleans for all computations of wins/losses in
>>> Valkyria.
>>> The hack is that every time a jigo occurs in a playout I randomize which
>>> player should win which means the change is very minor to the program.
>>> Thus
>>> I d not to model jigo more than in a statiscal sense.
>>>
>>> I am doing some very deep searches to see what could be good moves for
>>> the
>>> most important lines in the opening book. I am using the 7.5 komi book
>>> because it is the most developed, but it may be that a 5.5 book is better
>>> if
>>> black is favored by 7.0 komi.
>>>
>>> Anyway I just looked at a deep search spending 10hours of search, where
>>> old
>>> branches of the tree is cut off every time the program runs out of
>>> memory.
>>> (Which may also be a part of the problem). It turned out that search
>>> stuck
>>> to a PV and played to the end over and over again with considering other
>>> alternatives. Valkyria uses exploration = 0. The game it played over and
>>> over was 80 ply deep thanks to a small kofight and all move looks
>>> reasonable
>>> although sometimes some moves look a little odd. It does search
>>> alternatives
>>> a little bit in each position but it seems to quickly lock onto something
>>> giving 50% for sure.
>>>
>>> My question: Is this normal? Without Jigo this would not be possible
>>> because a deep variation to the absolute end of the game would be a clear
>>> loss for one side and then as the score goes down to 0 the search will
>>> explore many alternatives.
>>>
>>> Could it be that playing alternative moves in this search were all too
>>> risky so that search converges on a very long but guaranteed risk free
>>> jigo?
>>>
>>> I see this as a problem because if this happens a lot the program seems
>>> not
>>> really to search all options probably. It could be that my program has a
>>> bug
>>> or twoo that causes this problem, so therefor I am curious if anyone
>>> could
>>> reflect on it or have some experience.
>>>
>>> I believe chess programs uses something call "contempt" to avoid drawing
>>> too much (I think espeically against weaker opponents).
>>>
>>> Best
>>> Magnus
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>>>
>>
>
>
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