The GA-based NQueen Solver uses a modular 2APL multi-agent architecture where each agent encodes a candidate N-Queen configuration. Agents evaluate their fitness by counting non-attacking queen pairs, then share high-fitness configurations with others. Genetic operators—selection, crossover, and mutation—are applied across the agent population to generate new candidate boards. Over successive iterations, agents collectively converge on valid N-Queen solutions. The framework is implemented in Java, supports parameter tuning for population size, crossover rate, mutation probability, and agent communication protocols, and outputs detailed logs and visualizations of the evolutionary process.

BotPlayers is a versatile open-source framework designed to streamline the development and deployment of AI-driven game-playing agents. It features a flexible environment abstraction layer that supports screen scraping, web APIs, or custom simulation interfaces, allowing bots to interact with various games. The framework includes built-in reinforcement learning algorithms, genetic algorithms, and rule-based heuristics, along with tools for data logging, model checkpointing, and performance visualization. Its modular plugin system enables developers to customize sensors, actions, and AI policies in Python or Java. BotPlayers also offers YAML-based configuration for rapid prototyping and automated pipelines for training and evaluation. With cross-platform support on Windows, Linux, and macOS, this framework accelerates experimentation and production of intelligent game agents.