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https://github.com/pchampio/othello-prolog
:scroll: A fully functional Othello (Reversi) game, with several AIs, made in prolog for swipl.
https://github.com/pchampio/othello-prolog
alpha-beta artificial-intelligence board-game logic-programming minmax othello-game prolog student-project swipl univ-lemans
Last synced: 1 day ago
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:scroll: A fully functional Othello (Reversi) game, with several AIs, made in prolog for swipl.
- Host: GitHub
- URL: https://github.com/pchampio/othello-prolog
- Owner: pchampio
- License: bsd-3-clause
- Created: 2017-11-06T23:12:35.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-04-26T10:20:33.000Z (9 months ago)
- Last Synced: 2024-12-16T18:20:19.253Z (about 2 months ago)
- Topics: alpha-beta, artificial-intelligence, board-game, logic-programming, minmax, othello-game, prolog, student-project, swipl, univ-lemans
- Language: Prolog
- Homepage:
- Size: 1.43 MB
- Stars: 16
- Watchers: 3
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Othello Prolog
> A fully functional [Othello game](https://en.wikipedia.org/wiki/Reversi), with several AIs, made for the [swi prolog interpreter](http://www.swi-prolog.org/)
## Overview
This app allows 2 players to play an Othello game.
> During de pre-game menu the admin can set the two players to human or AIs.
>
>
> `-- Set the Player x to be a --`
> `1. Human` ---> User input. [prompt.pl](./game/prompt.pl)
> `2. Bot (random)` ---> A totally random AI. [random.pl](./ai/random.pl)
> `3. Bot (minmax)` ---> An AI using the min-max algorithm. [minmax.pl](./ai/minmax.pl)
> `4. Bot (alphabeta)` ---> An AI using the alpha-beta algorithm. [alphabeta.pl](./ai/alphabeta.pl)
## Usage
### Play
Load the file in the swipl-interpreter:
```
$ swipl ./othello.pl
```
Play the game:
```
>?- play.
```
## Things about this implementation
### Heuristic/Evaluation function
I used [this](https://courses.cs.washington.edu/courses/cse573/04au/Project/mini1/RUSSIA/Final_Paper.pdf)
excellent heuristic/evaluation function, made by researchers from the University of Washington.
This heuristic function is actually a collection of several heuristics
and calculates the utility value of a board position by assigning
different weights to those heuristics [here](https://github.com/Drakirus/othello-prolog/blob/05cecc989db8e5ec041380c2ba5f77377fa3f524/ai/heuristic.pl#L172).
The heuristic takes into account:
- The coin parity [here](https://github.com/Drakirus/othello-prolog/blob/05cecc989db8e5ec041380c2ba5f77377fa3f524/ai/heuristic.pl#L22-L34)
- The mobility [here](https://github.com/Drakirus/othello-prolog/blob/05cecc989db8e5ec041380c2ba5f77377fa3f524/ai/heuristic.pl#L37-L72)
- The corners captured [here](https://github.com/Drakirus/othello-prolog/blob/05cecc989db8e5ec041380c2ba5f77377fa3f524/ai/heuristic.pl#L77-L94)
- The stability [here](https://github.com/Drakirus/othello-prolog/blob/05cecc989db8e5ec041380c2ba5f77377fa3f524/ai/heuristic.pl#L99-L160)
### Performances
> To ensure the best **performance** and to improve the **bot playing time** the Game Engine use a [cache.pl](./utils/cache.pl) system.
The Cache saves the Heuristic value of a given board. [here](https://github.com/Drakirus/othello-prolog/blob/05cecc989db8e5ec041380c2ba5f77377fa3f524/ai/heuristic.pl#L183-L204).
This method saves (roughly):
> - **20000** call out of **36000** when running a AlphaBeta of Depth 4 (-1min30 save on my machine **2min10** vs **3min40**).
> - **66000** call out of **77000** when running a MinMax of Depth 4 (-9min save on my machine **11min** vs **20min**).
The Cache is also used to save some computing time of the [next board generation](https://github.com/Drakirus/othello-prolog/blob/89c9372dfe8d426c356f1e8153ece6a9e1db4295/game/rules.pl#L258-L269)
witch in his own cut the number of call to this rule by half (20 seconds save).
## MinMax Vs AlphaBeta (depth = 3)
Note that the overall trend in counts analyzed by Min-Max increases in the first
third following a Gaussian strongly pronounced. This is because the board
is free, and many combinations are allowed. In the next 2 thirds, the descent is
also done according to the distribution of gauss, since the
number of game combinations decreases gradually as we reach the
edges.
In depth 6 we have 3.8 million board
analyzed, to play an obvious shot find in depth 2.
Evidence appears: Alpha-Beta is still not the reincarnation of pure optimization
...
## Note
> I always write the test of the rule/predicate, below and indented by 3 tabs of the definition of itself.
## Todo
1. +/-inf heuristic when wining/losing. (some plays are more rewards than a winning one..)
2. Dynamic stability_weights
- The [stability_weights](https://github.com/Drakirus/othello-prolog/blob/d2a668b69f1844ba549eea9a6561505d8a57de9c/ai/heuristic.pl#L115) penalize the player making a move on a adjacent corners cell. But if the same player own the related corner it's actualy not a bad move..
3. Dynamic [weights](https://github.com/Drakirus/othello-prolog/blob/d2a668b69f1844ba549eea9a6561505d8a57de9c/ai/heuristic.pl#L172) in the dynamic_heuristic_evaluation
- At the start we need to reduce the opponent mobility
- In the middle game we need to focus on the stability of your placement
- In the very end of the game we only focus on the coin parity
4. Remove the ugly if/then/else
5. Make use of board symmetrys to improve the Cache systems
6. GnuProlog (2x faster)
