🎮 AI Pokemon Go
An AI-driven simulation where an agent navigates a maze to collect pokemons and hatch an egg, showcasing AI pathfinding and decision-making.
Achievements
Initial release of the AI Pokemon Go simulation, showcasing AI pathfinding and decision-making.
𝗦𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻
The project explores AI pathfinding and decision-making in a simulated environment, where an agent must navigate a maze to achieve specific goals.
𝗧𝗮𝘀𝗸
Develop an AI agent that can efficiently navigate a randomly generated maze, collect pokemons, and spend enough time in the maze for an egg to hatch.
𝗔𝗰𝘁𝗶𝗼𝗻
Implemented the simulation using Prolog, leveraging its logical programming capabilities to model the maze and agent behavior.
Defined rules and strategies for the agent to make decisions and navigate the maze effectively.
Utilized Prolog's pattern matching and backtracking features to explore different paths and optimize the agent's actions.
𝗥𝗲𝘀𝘂𝗹𝘁
The AI Pokemon Go simulation provides an engaging platform for studying AI pathfinding and decision-making, demonstrating the potential of AI in game-like scenarios.
Prolog's logical approach enabled the creation of a flexible and extensible simulation framework, facilitating further research and development in AI.