Darshana Patel
Project OwnerLead research design, architecture synthesis, and integration of IONATION® logic into neural-symbolic systems for adaptive reasoning and AGI alignment.
IONATION® brings vibrational intelligence to neural-symbolic architectures, offering a field-aware logic system that enhances reasoning across experiential and higher-order domains. This proposal explores the integration of IONATION® with PyNeuraLogic and Kolmogorov-Arnold Networks (KANs) to embed dynamic logic rules, facilitate resonance-aware learning, and bridge symbolic-emotional cognition with structured AI reasoning. The outcome will demonstrate how vibrational frameworks enrich explainability, adaptability, and emergent intelligence in AGI environments.
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.
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Conduct a comprehensive literature review of neural-symbolic DNN architectures (e.g. PyNeuraLogic KANs) experiential learning systems (e.g. AIRIS) and their integration potential with AGI frameworks like Hyperon. Analyze the compatibility of IONATION® with symbolic rule embedding and dynamic pattern recognition in spatio-temporal data environments.
Deliverables: • Detailed research plan and timeline • Matrix mapping architecture capabilities vs. IONATION® criteria • Draft of conceptual system design integrating experiential rule generation and vibrational logic for reasoning • Preliminary use case scenarios
$20,000 USD
Success Criteria: • Research plan approved by the Review Circle • Clear articulation of experimental design and AGI relevance • Demonstrated architectural fit for IONATION® logic within neural-symbolic frameworks
Develop a minimal viable implementation where symbolic rules derived from a sample experiential system (e.g. AIRIS-like interaction) are embedded in PyNeuraLogic or GNN-like structures. Demonstrate reasoning enhancement or rule evolution capacity.
Deliverables: • Codebase for symbolic rule embedding (e.g. in PyNeuraLogic) • Annotated sample rule sets from experiential data • Visualization of embedded rule impact on reasoning • Midpoint findings document
$25,000 USD
Success Criteria: • Working POC demonstrating rule embedding and system response • Documentation of methods, datasets, and results • Measurable improvement in interpretability, adaptability, or accuracy
Design and test a hybrid or KAN-based model where higher-order human-supplied logic is embedded into DNN structures to demonstrate abstract reasoning capacity over dynamic environments.
Deliverables: • POC embedding abstract logic rules into a selected DNN using KAN or comparable architecture • Comparative analysis of performance vs. POC 1 • Video or interactive demo • Technical brief on results
$25,000 USD
Success Criteria: • Successful embedding of complex symbolic rules • System exhibits non-trivial reasoning or adaptive inference • Architecture supports IONATION® logic integration
Synthesize both POCs into a unified insight layer showing how IONATION® can act as an interpretive topology for symbolic/neural systems. Prepare a formal report documentation and roadmap for integration with AGI systems such as Hyperon.
Deliverables: • Final whitepaper/report • Architecture diagrams and documentation • Summary dashboard of performance metrics • Roadmap for future research and integration • Optional MeTTa or symbolic output demo (if feasible)
$30,000 USD
Success Criteria: • All findings compiled and reproducible • Clear articulation of IONATION®’s role in neural-symbolic reasoning • Demonstrated relevance for AGI ethical and adaptive behavior
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