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ConceptForge: MeTTa Concept Blending Toolkit

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

ConceptForge is a MeTTa-based toolkit that demonstrates concept blending within the Hyperon/PRIMUS AGI framework. By manipulating atomic knowledge in the MeTTa language, it fuses two small concept maps into novel hybrid ideas. We will deliver both a CLI and a lightweight web UI for users to input example sets, generate blends, and inspect the underlying MeTTa code. Accompanying this prototype is a detailed specification mapping the blending algorithm to PRIMUS components. All code will be open‐sourced under GNU GPL for community adoption.

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
25 May 2025

Milestone 1 - Research & Design Plan

Description

Conduct a deep dive into formal concept analysis fuzzy/paraconsistent blending methods and MeTTa’s atomspace API. Define the input/output formats for concept lattices in MeTTa specify the core blending algorithm steps and map each step to PRIMUS/Hyperon components (ECAN DAS MeTTa).

Deliverables

1. Written research report on blending theory and MeTTa capabilities 2. Detailed specification document for the blending algorithm (pseudocode + MeTTa constructs) 3. Agile task breakdown with timeline for prototype development

Budget

$12,000 USD

Success Criterion

• Specification reviewed and approved by SingularityNET Hyperon study-group members • Clear mapping of algorithm to MeTTa/PRIMUS components • Agreement on input/output formats and performance targets

Milestone 2 - Prototype Implementation & Demo (MINI DEMO INSIDE)

Description

Mini concept demo: https://concept-forge.vercel.app/ Build the core ConceptForge module in MeTTa wrap it in a Node.js CLI for batch blending and create the React-based web UI. Implement unit tests example concept sets and basic performance logging. Ensure seamless end-to-end flow from input lattices to blended outputs.

Deliverables

1. MeTTa blending module 2. Node.js CLI (npm run blend) with test cases 3. Live web demo (Vercel URL) with preset scenarios and user uploads 4. Automated tests covering ≥80% of core logic

Budget

$24,000 USD

Success Criterion

• CLI successfully generates ≥5 example blends in under 1s each • Web UI displays blends and underlying MeTTa code correctly • Unit tests pass with ≥80% coverage • Peer feedback from at least 3 early users

Milestone 3 - Final Report Documentation & OSS Release

Description

Polish all code finalize user and developer documentation package the toolkit under GPL-3.0 and produce a comprehensive final report. Create integration guides for ECAN DAS and MeTTa along with recommended next steps and a roadmap for scaling to larger concept graphs.

Deliverables

1. Final written report (performance analysis design rationale future roadmap) 2. Developer docs (API reference integration guide) 3. User manual (CLI & UI usage example walkthroughs) 4. Open-source repo release tagged “v1.0” under GPL-3.0

Budget

$24,000 USD

Success Criterion

• Final report accepted by the RFP review panel • Documentation completeness verified • Public GitHub release live with correct licensing and CI badges • Positive initial community feedback (at least 3 GitHub stars/issues)

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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.

nblogist

Project Owner

Furqan Ahmed leads design, MeTTa algorithm development, TypeScript prototyping, UI/CLI implementation, and documentation—leveraging SingularityNET/Hyperon study-group calls for AGI guidance.

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