<p>I'm having trouble beating an AI in Isolation game: <a href="https://en.wikipedia.org/wiki/Isolation_(board_game)" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Isolation_(board_game)</a> with 3 queens on a 7x7 board. I tried applying alpha beta iteration with a scoring function on the state. I tried 3 scoring functions:</p>
<ol>
<li>(0.75/# moves taken) * number of legal moves - (# moves taken/0.75) * number of opponent legal moves</li>
<li>number of legal moves - 3 * number of opponent legal moves</li>
<li>3 * number of legal moves - number of opponent legal moves</li>
</ol>
<p>The first is an annealed aggressive strategy so the agent gets more aggressive as the game goes longer. The 2nd is a pure aggression strat and the last is a pure defensive strat. None of them consistently beat standard alpha beta iteration with the state scoring function: number of legal moves -  number of opponent legal moves. They all broke roughly even.</p>
<p>Any suggestions of scoring state functions or search algorithms are appreciated. The search has a limit of 6000 seconds per turn though.</p>

CC BY-SA 4.0
2021-02-08T10:53:12.527Z
What is your main **specific** question here? Please, choose one specific question and put it in the body **and** title of your post to clarify what you're asking here.
nbro
CC BY-SA 4.0
2021-02-08T16:56:11.250Z
I'm simply asking for a search algorithm with a cost function that can do better than iterative alpha beta
user8714896
CC BY-SA 4.0
2021-02-09T17:31:27.040Z
Is your question somehow related to this: https://ai.stackexchange.com/q/5174/2444?
nbro
<alpha-beta-pruning><iddfs>
Beating iterative alpha beta search in Isolation Game
user8714896
# Beating iterative alpha beta search in Isolation Game

(Asked by: username_0 on 2021-02-07)

I'm having trouble beating an AI in Isolation game: <https://en.wikipedia.org/wiki/Isolation_(board_game)> with 3 queens on a 7x7 board. I tried applying alpha beta iteration with a scoring function on the state. I tried 3 scoring functions:

1. (0.75/# moves taken) \* number of legal moves - (# moves taken/0.75) \* number of opponent legal moves
2. number of legal moves - 3 \* number of opponent legal moves
3. 3 \* number of legal moves - number of opponent legal moves

The first is an annealed aggressive strategy so the agent gets more aggressive as the game goes longer. The 2nd is a pure aggression strat and the last is a pure defensive strat. None of them consistently beat standard alpha beta iteration with the state scoring function: number of legal moves - number of opponent legal moves. They all broke roughly even.

Any suggestions of scoring state functions or search algorithms are appreciated. The search has a limit of 6000 seconds per turn though.

**Tags:** alpha-beta-pruning, iddfs

> **Comments:**
> **username_1**: What is your main **specific** question here? Please, choose one specific question and put it in the body **and** title of your post to clarify what you're asking here.  
> **username_0**: I'm simply asking for a search algorithm with a cost function that can do better than iterative alpha beta  
> **username_1**: Is your question somehow related to this: https://ai.stackexchange.com/q/5174/2444?  

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## 0 answers

