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.

ASP-DALI provides a unified platform for defining and executing logic-based intelligent agents. Developers write ASP rules to represent agent knowledge and goals, while DALI constructs define event reactions and action executions. At runtime, an ASP solver computes answer sets that guide the agent’s decisions, enabling it to plan, react to incoming events, and adjust beliefs dynamically. The framework supports modular knowledge bases, facilitating incremental updates and clear separation between declarative rules and reactive behaviors. ASP-DALI is implemented in Prolog with interfaces to popular ASP solvers, simplifying integration and deployment across research and prototype scenarios.