OpenNARS is built upon the principles of Non-Axiomatic Logic, enabling the system to perform deduction, induction, and abduction using truth-value pairs that reflect uncertainty. It maintains an experience-based memory of statements and dynamically recruits inference rules based on available resources, ensuring robust performance in real-time environments. The engine’s belief revision mechanism updates confidences as new information arrives, improving decision accuracy. Developers can integrate OpenNARS via provided SDKs in Java, C++, Python, JavaScript, Dart, or Go, and deploy it on desktops, servers, mobile devices, or embedded systems. Typical applications include cognitive robotics, autonomous agents, and complex problem-solving tasks where adaptive learning and efficient knowledge management are essential.

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.