Short summary

This RFP invites proposals to explore and demonstrate the use of neural-symbolic deep neural networks (DNNs), such as PyNeuraLogic and Kolmogorov Arnold Networks (KANs), for experiential learning and/or higher-order reasoning. The goal is to investigate how these architectures can embed logic rules derived from experiential systems like AIRIS or user-supplied higher-order logic, and apply them to improve reasoning in graph neural networks (GNNs), LLMs, or other DNNs. Bids are expected to range from $40,000 - $100,000.

Main purpose

• Improve human interpretability and explainability of AI systems;
• Can learn from small data using symbolic knowledge and reasoning;
• Bridge the gap between data-driven and symbolic reasoning;
• Could solve problems in domains in which structured learning is critical (e.g. medical ontologies).