Comprehensive literature review and comparative analysis of neural-symbolic architectures (PyNeuraLogic and KANs). This includes Initial experiments setup for both Proofs of Concept (POC).

Research plan, comparative analysis framework, and initial experimental setup.

$12,000 USD

1. Research Plan Completion: • Clear definition of project goals, timelines, and methodologies collaboratively developed. • Approval-ready research document outlining key milestones and deliverables. 2. Comparative Analysis Framework: • Established criteria and methods for objectively comparing PyNeuraLogic and KAN architectures. • Defined metrics for interpretability, scalability, reasoning quality, and system performance. 3. Initial Experimental Setup: • Functional experimental environment and infrastructure ready for testing and evaluation. • Preliminary testing scripts or code configurations demonstrating readiness for immediate experimentation.

POC-A (PyNeuraLogic): Integration of experiential logic rules (AIRIS-derived) into Graph Neural Networks demonstrating enhanced adaptive reasoning, guided by AI collaborator. POC-B (KANs): Higher-order symbolic reasoning for predictive modeling (e.g., smart-grid scenario) demonstrating superior interpretability and performance on structured, continuous data, developed through collaborative iterative refinements.

Implementation and preliminary evaluation of both POCs, supported by detailed AGI methodological guidance.

$24,000 USD

1. Completion of Implementations: • Successfully embedding experiential logic rules using PyNeuraLogic into GNNs. • Successfully integrating higher-order symbolic reasoning using KANs into a structured predictive model (e.g., smart-grid scenario). 2. Preliminary Evaluation Results: • Providing initial quantitative or qualitative evidence that demonstrates functional enhancements in reasoning capabilities compared to baseline architectures. 3. Application of AGI Methodology: • Clear documentation showing adherence to and integration of AGI best practices provided through detailed methodological guidance. • Demonstrable alignment between theoretical guidance and practical implementations. 4. Reproducibility: • Preliminary code, methods, and results documented clearly enough to replicate initial findings.

Final collaborative comparative evaluation, interactive demonstrations, comprehensive documentation, and final report.

Perform AI-supported comparative analyses demonstrating strengths, limitations, and optimal use-cases of each architecture. • Provide interactive visualization tools and detailed documentation developed collaboratively for reproducibility and extensibility.

$24,000 USD

1. Comparative Evaluation: • Completion of detailed performance analysis clearly comparing strengths and limitations of PyNeuraLogic and KAN architectures. • Demonstrable evidence of improved reasoning, interpretability, and adaptability validated by empirical data. 2. Interactive Demonstrations: • Fully functional visual tools or interfaces clearly illustrating the operational benefits and differences of each architecture. • User-friendly demonstrations enabling stakeholders to explore system reasoning dynamically. 3. Comprehensive Documentation: • Detailed technical documentation ensuring reproducibility, extensibility, and ease of future research. • Explicit documentation of methodology, datasets, results, and software dependencies. 4. Final Report: • Submission of a comprehensive report summarizing methods, outcomes, insights, recommendations, and clear alignment with project objectives. • Professional-quality document clearly articulating results, lessons learned, and potential next steps for future work.