A command-line interface for running Tic-Tac-Toe - from the project Over-Engineering Tic-Tac-Toe built using modern Java Frameworks: Quarkus/PicoCLI/GraalVM for development speed and native execution.
Supports algorithm options -p <algo>:
- RANDOM: Chooses the next move at random
- MINIMAX: Chooses the next move using minimax
- MINIMAX: Chooses the next move using minimax w. alpha-beta pruning
- MAXN: Chooses the next move using MaxN
- PARANOID: Chooses the next move using Paranoid "me vs. the world"
- MCTS: Choose the next move using monte carlo tree search
Usage:
Play a game of tic-tac-toe.
tictactoe [-hnsV] [-d=<dimension>] -m=<marker> [-md=<maxDepth>]
[-mi=<maxIterations>] [-mt=<maxTimeMillis>] [-p=<player>]
Description:
A game of Tic-Tac-Toe that can be played on an arbitrary sized board by humans
and/or AI opponents.
When started without any arguments, the game will be played on a 3x3 board with
two random bot players.
Options:
-d, --dimension=<dimension>
The dimension of the game board (default: 3, for a
3x3 board).
-s, --serialization-enabled
Serialized persistence enabled (default: false).
-n, --native Use native game board for enhanced performance if
available (default: false).
-h, --help Show this help message and exit.
-V, --version Print version information and exit.
A player participating in the game denoted by their marker e.g. ['X', 'O', ...]
by default two random bots 'X' and 'O' will play.
-m, --marker=<marker> The marker of the player, e.g. 'X', 'O'.
-p, -a, --player, --algorithm=<player>
The player/algorithm type, one of HUMAN, MINIMAX,
ALPHABETA, PARANOID, RANDOM, MCTS (default: RANDOM).
- Example 1: `-p HUMAN -m X -p RANDOM -m O` will
create a game with a human player and a random bot
player.
- Example 2: `-p HUMAN -m X -p PARANOID -m O -p
RANDOM -m W` will create a game with a human player
and two bot players using paranoid and random
respectively.
- Example 3: `-p MCTS -mi 1000 -m O -p RANDOM -m X`
will create a game with a mcts bot player with a
maximum number of iterations of 1000 vs. a random
bot player.
- Example 4: `-mO -mX` will create a game with two
random bot players.
- Example 5: `-mX -mO -p MCTS -mi 500` will create a
game with a random bot and a mcts bot player with a
maximum number of iterations of 500.
-mi, --max-bot-iterations=<maxIterations>
The maximum number of iterations for bot player types.
-md, --max-bot-depth=<maxDepth>
The maximum depth of search for bot player types.
-mt, --max-bot-time=<maxTimeMillis>
The maximum time in millis to determine next move for
bot player types.# Simply run the game with a bot player 'X' facing another bot player 'O'.
tictactoe-cli
# Play a game with an intelligent bot player 'X' using monte carlo tree search
# with a maximum algo time of 500ms facing a bot player 'O' using a random strategy.
tictactoe-cli -mX p MCTS -mt 500 -mO
# Play a game with a human player 'O' facing an intelligent bot player 'X' using
# monte carlo tree search with a maximum number of iterations of 1000.
tictactoe-cli -mO -p HUMAN -mX p MCTS -mi 1000# ❯ tictactoe-cli
Players: X, O ([Local{playerMarker=X, player=BotPlayer[strategyFunction=org.xxdc.oss.example.bot.BotStrategy$$Lambda/0x000001fe013d3c68@4e7afe5a]}, Local{playerMarker=O, player=BotPlayer[strategyFunction=org.xxdc.oss.example.bot.BotStrategy$$Lambda/0x000001fe013d3c68@4e7afe5a]}])
- TicTacToeClient/1.0 [Local (X:BotPlayer)] (IP: 127.0.0.1; Host: corbinm1mac.local; Java: 23; OS: Mac OS X 14.6.1)
- TicTacToeClient/1.0 [Local (O:BotPlayer)] (IP: 127.0.0.1; Host: corbinm1mac.local; Java: 23; OS: Mac OS X 14.6.1)
___
___
___
__X
___
___
_OX
___
___
_OX
X__
___
_OX
X_O
___
XOX
X_O
___
XOX
X_O
_O_
XOX
XXO
_O_
XOX
XXO
_OO
Winner: Player X!
XOX
XXO
XOOUsing Hyperfine to benchmark the native executable vs. the Java HotSpot executable:
# Native run vs. Java HotSpot run
❯ hyperfine --warmup 1 -n native 'tictactoe-cli/build/tictactoe-cli-1.2.1-SNAPSHOT-runner' -n hotspot 'java -jar tictactoe-cli/build/quarkus-app/quarkus-run.jar'
Benchmark 1: native
Time (mean ± σ): 22.6 ms ± 7.5 ms [User: 6.6 ms, System: 8.9 ms]
Range (min … max): 17.5 ms … 88.8 ms 117 runs
Warning: Statistical outliers were detected. Consider re-running this benchmark on a quiet system without any interferences from other programs. It might help to use the '--warmup' or '--prepare' options.
Benchmark 2: hotspot
Time (mean ± σ): 511.1 ms ± 21.3 ms [User: 928.0 ms, System: 99.1 ms]
Range (min … max): 490.6 ms … 553.7 ms 10 runs
Warning: Statistical outliers were detected. Consider re-running this benchmark on a quiet system without any interferences from other programs. It might help to use the '--warmup' or '--prepare' options.
Summary
native ran
22.57 ± 7.51 times faster than hotspot
❯ uname -a
Darwin corbinm1mac.local 23.6.0 Darwin Kernel Version 23.6.0: Mon Jul 29 21:14:30 PDT 2024; root:xnu-10063.141.2~1/RELEASE_ARM64_T6000 arm64