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Made AI stronger by working on Minimax/AB pruning #2
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- Previously, I had omitted movable tigers parameter from evaluation function but AI was defeated when I played with trapping tigers strategy. Then, I integrated it into the evaluation function again.
- Problem which I thought was created by that movable tiger thing was actually because of the initial value(value) thing. So, again I included and the AI became better.
# find the minimum attainable value for the minimizer
if not is_max:
value = self.INF
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Why replace INF?
To make the value variable as small as we can. Previously, it was equal to
the value of the win/loss state. Ideally, it should be +/- infinity and
the win/loss state should be some finite large number. We have made the
win/loss state some finite large number(though written as INF). So, we
need to make the value variable smaller/larger than the INF variable.
... On 2018年9月24日 at 22:21, Ashish Acharya ***@***.***> wrote:
***@***.**** commented on this pull request.
------------------------------
In Engine.py
<#2 (comment)>:
> @@ -37,7 +38,7 @@ def minmax(self, is_max=True, depth=0, alpha=-INF, beta=INF):
# find the minimum attainable value for the minimizer
if not is_max:
- value = self.INF
Why replace INF?
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Made AI stronger by implementing and modifying the following things.