Introduction: This project aimed to develop a pocket tank game simulation model using NetLogo software and Scala language as the backend. The project was undertaken by a team of four students with expertise in agent-based artificial intelligence (AI). The objective was to create an interactive and challenging game environment where AI-controlled tanks could strategize and compete against each other.
Methodology:
Game Design and Development: The team designed and developed a pocket tank game with intuitive controls and a user-friendly interface. The game incorporated realistic physics and mechanics to enhance the gameplay experience.
Agent-Based AI Implementation: The core focus of the project was to create intelligent AI agents for the tanks. The team utilized NetLogo, a powerful multi-agent simulation environment, to implement the AI algorithms. Scala programming language was employed to handle the backend processing and logic.
AI Decision-Making: The AI agents were programmed to make strategic decisions based on various factors, including the positions of opponents, available ammunition, terrain conditions, and their own tank's health. The team implemented sophisticated algorithms, such as pathfinding, target prioritization, and trajectory prediction, to enhance the AI's decision-making capabilities.
Learning and Adaptation: To further improve the AI agents' performance, the team incorporated machine learning techniques. The AI agents were trained using reinforcement learning algorithms, allowing them to learn from their experiences and adapt their strategies over time. This enabled the agents to become more proficient and challenging opponents as the game progressed.
Results and Evaluation: The developed pocket tank game simulation model successfully showcased the team's efforts in implementing agent-based AI techniques. The AI-controlled tanks exhibited intelligent decision-making, realistic behaviors, and adaptive strategies. The gameplay experience was engaging and challenging, providing players with an immersive and enjoyable experience.
Throughout the development process, the team conducted rigorous testing and evaluation to identify and address any bugs, performance issues, or imbalances in the game mechanics. User feedback and playtesting were also utilized to refine and optimize the game's features, controls, and overall experience.
Conclusion: In conclusion, the pocket tank game simulation model developed by the team effectively demonstrated the integration of agent-based AI techniques using NetLogo and Scala. The project highlighted the team's proficiency in game design, AI algorithm implementation, and machine learning. The resulting game provides an entertaining and competitive experience for players while showcasing the potential of AI in gaming environments.