MeTTa-NARS (legacy duplicate)

MeTTa-NARS (Non-Axiomatic Reasoning System)

Responsible: Peter Isaev, Patrick Hammer

GitHub / Demos:

• https://github.com/patham9/metta-nars
• Interactive IQ tests
• MeTTa-NARS controlling a small transbot robot running ROS1

Papers:

• Isaev, P., & Hammer, P. (2025). NARS-GPT: An Integrated Reasoning System for Natural Language Interactions. Intelligent Systems and Applications, Springer Nature.
• Hammer, P., Isaev, P., et al. (2024). Non-Axiomatic Reasoning for an Autonomous Mobile Robot. IEEE ICRA 2024.

Description:

MeTTa-NARS is an open-ended uncertainty reasoning engine designed to operate under the Assumption of Insufficient Knowledge and Resources (AIKR). Unlike traditional logical systems that require complete, clean data, MeTTa-NARS is built for the open world where information is scarce, inconsistent, and constantly changing.

The system distinguishes itself through its usage of Non-Axiomatic Logic (NAL), which replaces binary truth with a two-dimensional evidence value (frequency and confidence). This allows the agent to distinguish between "I know this is true because I have seen it 100 times" and "I think this is true, but I have only seen it once." MeTTa-NARS manages this knowledge via a concept-centric memory and a rigorous inference control mechanism that treats reasoning as a resource allocation problem.

MeTTa-NARS drives the active, never-ending learning loop, allowing agents to continually refine their understanding of the world as they encounter new, unexpected phenomena.

Roadmap:

• Further improved Attention Allocation
• Improvement of temporal reasoning by enlarging data-structures
• More effective handling of procedural information for robust decision-making