aasavravi1234
Project OwnerLeading the design and development of neuro-symbolic DNN architectures, embedding logic rules for experiential learning & higher-order reasoning, and ensuring successful implementation and validation
Our proposal explores neuro-symbolic DNN architectures like PyNeuraLogic and Kolmogorov-Arnold Networks (KANs) for enhancing experiential learning and higher-order reasoning. By embedding logic rules from systems like AIRIS or user-supplied abstract rules, we aim to improve reasoning, explainability, and small-data learning. The project includes comparative analysis, proof of concept, and real-world applications in dynamic systems and structured domains, advancing AI’s ability to reason and adapt effectively.
This RFP invites proposals to explore and demonstrate the use of neuro-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.
In order to protect this proposal from being copied, all details are hidden until the end of the submission period. Please come back later to see all details.
Develop foundational components for integrating logic rules from AIRIS or higher-order reasoning into neuro-symbolic DNNs like PyNeuraLogic and KANs. This includes initial architecture design, logic rule embedding, and basic compatibility with frameworks like GNNs and LLMs.
Initial neuro-symbolic architecture. Embedded basic rules for experiential learning or higher-order reasoning. Documentation for system architecture and integration processes.
$20,000 USD
Successful embedding of basic symbolic logic rules into PyNeuraLogic or KANs, demonstrated by reasoning tests achieving >70% accuracy on predefined datasets.
Enhance rule embedding for dynamic systems by refining integration processes and optimizing logic processing. Includes creating mechanisms for real-time reasoning and adaptability for both experiential learning and abstract hierarchical logic tasks.
Optimized rule embedding methods. Tools for reasoning over dynamic systems. Comparative analysis of experiential learning vs. higher-order reasoning approaches.
$30,000 USD
Demonstrated reasoning improvements with a 20% reduction in computation costs and enhanced adaptability across multiple test scenarios.
Conduct validation and benchmarking of the integrated neuro-symbolic DNN systems in real-world applications, such as medical ontology reasoning or dynamic decision-making in smart grids. Publish results and refine based on findings.
Benchmarking reports and performance comparisons. Reproducible codebase with detailed documentation. Research paper submission to peer-reviewed venues.
$30,000 USD
Validation shows >30% improvement in reasoning efficiency and explainability, with results reproduced and independently verified by external researchers.
Reviews & Ratings
Please create account or login to write a review and rate.
Check back later by refreshing the page.
© 2025 Deep Funding
Join the Discussion (0)
Please create account or login to post comments.