Darshana Patel

Project Owner

IONATION®:Vibrational Logic for Emergent Concepts

Expert Rating

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Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Experiment with concept blending in MeTTa

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Overview

IONATION®: Vibrational Logic for Emergent Concepts introduces a multidimensional lens for symbolic concept blending within MeTTa and Hyperon. Grounded in vibrational intelligence, IONATION® enhances concept creation by guiding emergent abstraction through flow, coherence, and intuitive logic. It supports fuzzy and paraconsistent reasoning by modeling how concepts arise through energetic tension, resonance, and resolution. This approach augments formal concept analysis with creative structure, enabling Hyperon to synthesize novel, meaningful concepts across contexts. The project will include framework design, MeTTa implementation, and a creativity evaluation protocol.

RFP Guidelines

Experiment with concept blending in MeTTa

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $100,000 USD
Proposals 12
Awarded Projects n/a

SingularityNET

Apr. 14, 2025

This RFP seeks proposals that experiment with concept blending techniques and formal concept analysis (including fuzzy and paraconsistent variations) using the MeTTa programming language within OpenCog Hyperon. The goal is to explore methods for generating new concepts from existing data and concepts, and evaluating these processes for creativity and efficiency. Bids are expected to range from $30,000 - $60,000.

Proposal Description

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Total Milestones
3
Total Budget
$60,000_USD
Last Updated
19 May 2025

Milestone 1 - Design IONATION® Concept Blending Ontology

Description

Develop the theoretical and architectural design of the IONATION® concept formation model tailored for integration with Hyperon and MeTTa. This includes formal mappings of vibrational logic to symbolic representations and paraconsistent/FCA-aligned blending processes.

Deliverables

Deliverables: • Research plan + methodology • Vibrational concept blending framework (v1) • Diagrams + symbolic structure translation schema • Integration plan for MeTTa

Budget

$12,000 USD

Success Criterion

Success Criteria: • Comprehensive documentation and visual model of the concept blending system • Peer feedback from MeTTa study group or AGI forum • Internal testing of symbolic alignment logic and feasibility in MeTTa

Milestone 2 - Prototype & Implement Concept Gen Algorithms

Description

Prototype & Implement Vibrational Concept Generation Algorithms in MeTTa Translate the IONATION® framework into working MeTTa code. Develop at least one concept generation module using fuzzy/paraconsistent formal logic with resonance-based coherence scoring.

Deliverables

Deliverables: • MeTTa implementation files • Concept generation prototype code • Fuzzy/concept blend logic examples • Initial integration into Distributed Atomspace (DAS)

Budget

$24,000 USD

Success Criterion

Success Criteria: • Working MeTTa program with output demonstrating emergent concept creation • Successful atom-level generation and integration within test Atomspace • Review/feedback from SingularityNET or OpenCog teams

Milestone 3 - Evaluate Creativity Finalize Publish Report

Description

Evaluate novelty coherence and creativity of generated concepts using both IONATION’s resonance metrics and optional LLM-based comparison. Package results into a final framework with implementation guidance and proposed extensions.

Deliverables

Deliverables: • Concept evaluation protocol • Side-by-side scoring of generated concepts • Final report with visuals roadmap and documentation • Recommendations for integration with ECAN/DAS/PLN

Budget

$24,000 USD

Success Criterion

Success Criteria: • Concept evaluation demonstrates practical creativity and logical novelty • Proposal shows value-added to AGI concept formation • Final report delivered and publishable for SingularityNET ecosystem

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The weighted average of the 4 perspectives Overall

0.0
Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

0.0

Review Headline

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary) Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference? Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary) This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?” For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?). For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things? Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Darshana Patel

Project Owner

Principal Investigator and Ontological Framework Architect. Responsible for research design, framework modeling, MeTTa integration strategy, and overall synthesis of deliverables.

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