In this project, students will develop an adversarial search agent to play the game "Isolation". Isolation is a deterministic, two-player game of perfect information in which the players alternate turns moving a single piece from one cell to another on a board. Whenever either player occupies a cell, that cell becomes blocked for the remainder of the game. The first player with no remaining legal moves loses, and the opponent is declared the winner. These rules are implemented in the isolation.Board class provided in the repository.
This project uses a version of Isolation where each agent is restricted to L-shaped movements (like a knight in chess) on a rectangular grid (like a chess or checkerboard). The agents can move to any open cell on the board that is 2-rows and 1-column or 2-columns and 1-row away from their current position on the board. Movements are blocked at the edges of the board (the board does not wrap around), however, the player can "jump" blocked or occupied spaces (just like a knight in chess).
Additionally, agents will have a fixed time limit each turn to search for the best move and respond. If the time limit expires during a player's turn, that player forfeits the match, and the opponent wins.
To get started with this project,
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Run the following command to see the sample players for this game.
python sample_players.py -
To conduct the tournament,run the following command.
python tournament.py -
To implement any custom heuristics, try to modify the file names as
game_agent.py.
The tournament.py script is used to evaluate the effectiveness of the custom heuristics. The script measures relative performance of your agent (named "Student" in the tournament) in a round-robin tournament against several other pre-defined agents. The Student agent uses time-limited Iterative Deepening along with your custom heuristics.
The performance of time-limited iterative deepening search is hardware dependent (faster hardware is expected to search deeper than slower hardware in the same amount of time). The script controls for these effects by also measuring the baseline performance of an agent called "ID_Improved" that uses Iterative Deepening and the improved_score heuristic defined in sample_players.py. Your goal is to develop a heuristic such that Student outperforms ID_Improved.
The tournament opponents are listed below. (See also: sample heuristics and players defined in sample_players.py)
- Random: An agent that randomly chooses a move each turn.
- MM_Open: MinimaxPlayer agent using the open_move_score heuristic with search depth 3
- MM_Center: MinimaxPlayer agent using the center_score heuristic with search depth 3
- MM_Improved: MinimaxPlayer agent using the improved_score heuristic with search depth 3
- AB_Open: AlphaBetaPlayer using iterative deepening alpha-beta search and the open_move_score heuristic
- AB_Center: AlphaBetaPlayer using iterative deepening alpha-beta search and the center_score heuristic
- AB_Improved: AlphaBetaPlayer using iterative deepening alpha-beta search and the improved_score heuristic
The isoviz folder contains a modified version of chessboard.js that can animate games played on a 7x7 board. In order to use the board, you must run a local webserver by running python -m http.server 8000 from your project directory (you can replace 8000 with another port number if that one is unavailable), then open your browser to http://localhost:8000 and navigate to the /isoviz/display.html page. Enter the move history of an isolation match (i.e., the array returned by the Board.play() method) into the text area and run the match. Refresh the page to run a different game. (Feel free to submit pull requests with improvements to isoviz.)
Once your project has been reviewed and accepted by meeting all requirements of the rubric, you are invited to complete the competition_agent.py file using any combination of techniques and improvements from lectures or online, and then submit it to compete in a tournament against other students from your cohort and past cohort champions.