JacobBillings
Project OwnerAs a complex systems scientist, with over 20 y of experience, Dr. Billings is an independent researcher capable of meeting and exceeding all milestones of the current proposal in a timely fashion.
This proposal presents an approach to machine cognition using PyNeuraLogic in an unsupervised neural-symbolic architecture. The core innovation treats knowledge graphs as dense weighted networks, enabling the discovery of latent logical relationships between entities. The implementation follows two stages: template bootstrapping with established knowledge bases, followed by batched introduction of new entities. Through message passing and weight updates, the system infers potential relationships beyond explicit definitions. The research concludes by evaluating how this template enhances Large Language Model reasoning compared to standard retrieval augmented generation techniques.
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.
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Establish the foundational architecture for template learning using PyNeuraLogic. This phase focuses on implementing the core message passing and weight update mechanisms within the GNN framework. The implementation will include the basic infrastructure for treating knowledge graphs as dense weighted networks setting up the computational pipeline for parallel message passing and developing the initial weight update process.
- Documented PyNeuraLogic implementation of the dense knowledge graph architecture - Message passing cycle implementation with parallel processing capability - Weight update process implementation with gradient tracking - Unit tests covering core functionality - Technical documentation detailing system architecture and component interaction - Code repository with version control and documentation
$7,000 USD
- Successful implementation of message passing mechanism with demonstrable convergence - Weight update process showing stable learning behavior - System successfully processes small-scale knowledge graphs - All unit tests passing with >90% code coverage - Technical documentation peer-reviewed and approved - Code review completed with all critical issues resolved
Develop and implement the ontology bootstrapping system using established knowledge bases. This phase involves creating interfaces to major knowledge bases (DBpedia NELL SingularityNET KB) implementing the entity introduction mechanism and establishing the batched training process for new entity integration.
- Knowledge base integration interfaces - Entity introduction mechanism implementation - Batched training process for new entities - Integration tests for knowledge base connections - Documentation of knowledge base integration protocols - Performance benchmarks for entity introduction
$5,600 USD
- Successful integration with at least three major knowledge bases - Entity introduction mechanism demonstrating consistent performance - Batched training process showing stable learning curves - Integration tests passing with >90% success rate - Documentation verified by independent technical review - Performance benchmarks meeting or exceeding baseline metrics
Implement and execute comprehensive validation procedures for the template learning system. This phase focuses on link-prediction benchmarks knowledge base completion tasks and systematic evaluation of the system's ability to discover higher-order logical patterns.
- Implementation of validation framework - Benchmark results on FB15k-237 and WN18RR datasets - Analysis of higher-order logical pattern discovery - Validation test suite - Comprehensive performance analysis report - Validation methodology documentation
$5,600 USD
- Mean Reciprocal Rank (MRR) and Hits@k metrics exceeding baseline models - Successful completion of knowledge base completion tasks - Demonstrated discovery of non-trivial higher-order logical patterns - Validation tests showing reproducible results - Performance analysis showing statistical significance - Independent verification of validation methodology
Develop and implement the bidirectional interface between the template learning system and Large Language Models. This phase involves creating the RAG-like query system implementing the logical predicate conversion mechanism and developing the template-based reasoning enhancement system.
- LLM integration interface - Query-to-predicate conversion system - Template-based reasoning enhancement implementation - Comparative analysis versus standard RAG systems - Integration testing suite - System performance documentation - End-to-end demonstration system
$9,800 USD
- Successful bidirectional communication between LLM and template system - Accurate conversion of natural language queries to logical predicates - Demonstrable improvement in semantic reasoning capabilities - Comparative analysis showing advantages over standard RAG - All integration tests passing - System performance meeting or exceeding specified requirements - Successful demonstration of end-to-end system functionality
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