Non Axiomatic Reasoning

Non-axiomatic reasoning investigates methods for logical inference that move beyond the limitations of classical, axiomatic systems, aiming to create more flexible and adaptable reasoning systems. Current research focuses on developing and applying models like the Non-Axiomatic Reasoning System (NARS) and integrating them with machine learning techniques such as conformal prediction and reinforcement learning to address challenges in knowledge graph embedding, natural language processing, and artificial general intelligence. This field is significant because it seeks to build AI systems capable of commonsense reasoning, handling incomplete or contradictory information, and adapting to changing environments, with potential applications in diverse areas like robotics, healthcare, and web automation.