As part of the Udacity AI Nanodegree, I developed 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.
I implemented functions in the game_agent.py file to complete this project. These functions are:
MinimaxPlayer.minimax(): minimax search algorithmAlphaBetaPlayer.alphabeta(): minimax search with alpha-beta pruningAlphaBetaPlayer.get_move(): iterative deepening searchcustom_score(): a custom evaluation function. This is supposed to be my best position evaluation heuristiccustom_score_2(): an alternate position evaluation heuristic to "custom_score"custom_score_3(): an alternate position evaluation heuristic to "custom_score"
The tournament.py script is used to evaluate the effectiveness of my custom heuristics. The script measures relative performance of my agent (named "Student" in the tournament) in a round-robin tournament against several other pre-defined agents. The agent uses time-limited Iterative Deepening along with my 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. The goal of the project was 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