The GameBot-AI-Agent project focuses on the development of an intelligent game-playing agent for the Pente game, an abstract strategy board game. The objective is to create a competitive AI agent capable of making strategic decisions to outperform other agents in a competition setting.
- An evaluation function was designed to analyze the strategic configurations of the game board, assigning values based on desirability or advantage for the AI agent.
- Factors considered may include the number of pieces in a row, potential threats, and overall board control.
- Alpha-beta pruning, a search algorithm, was implemented to optimize decision-making by reducing the number of nodes evaluated in the game tree.
- This enhances the agent's efficiency in exploring possible moves and focusing on promising paths.
- The sliding window technique was employed to enhance gameplay performance, efficiently assessing and updating the evaluation function over different sections of the game board.
- This technique is commonly used in optimization to process data in overlapping segments.
- Tested in a competition setting against over 200 other AI agents.
- Achieved an impressive 12th ranking, demonstrating the agent's capability to outsmart a significant number of competing agents.
The GameBot-AI-Agent successfully integrates advanced AI techniques, including an efficient evaluation function, alpha-beta pruning, and the sliding window technique. Its strong performance in the competition reflects its ability to make strategic decisions in the dynamic and complex environment of the Pente game.