Pragadeesh

Project Owner

Recursive Concept Blending in MeTTa

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

This proposal explores recursive concept blending in MeTTa using symbolic compression and paraconsistent FCA. It aims to build interpretable, logic-driven concept generation engines with a lightweight LLM-based evaluation loop. Deliverables include modular MeTTa code, open-source documentation, and integration-ready components for OpenCog Hyperon, all completed within 6 months.

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
7 May 2025

Milestone 1 - Architecture & Planning Phase

Description

Duration: 2 Weeks Covers research design symbolic layout module flow Fastest phase as most groundwork is mentally ready 🗓️ Estimated Timeline: Week 1 to Week 2 This phase includes detailed system architecture for symbolic concept blending in MeTTa definition of symbolic structures transformation logic and test scaffolds for recursive logic application. A structured breakdown of module design and interaction flow will be produced along with a work timeline and integration layout for Hyperon compatibility.

Deliverables

- Project design document (architecture overview) - Module flowchart (symbolic blending FCA memory) - Technical spec sheet (AtomSpace structures MeTTa templates) - Evaluation criteria and test case list

Budget

$15,000 USD

Success Criterion

- Clear, publishable architecture delivered - Full design spec approved by internal review - Ready-to-code blueprint for MeTTa module implementation - Passed initial logical consistency validation

Milestone 2 - Module Development & Initial Testing

Description

Duration: 10 Weeks Heaviest dev phase: MeTTa FCA compression logic Requires deep testing and refinement 🗓️ Estimated Timeline: Week 3 to Week 12 This milestone covers the full development of the symbolic concept blending engine in MeTTa implementation of fuzzy and paraconsistent Formal Concept Analysis (FCA) and a symbolic memory compression module. Each module will be integrated with a lightweight test runner and preliminary test cases will be executed for logical validation.

Deliverables

- Symbolic blending logic implemented in MeTTa - FCA logic engine with fuzzy consistency support - Symbolic memory compression layer (graph pattern reduction) - Internal test suite + initial evaluation report

Budget

$30,000 USD

Success Criterion

- All three modules completed and integrated - Passes internal test suite with over 90% logic consistency - Codebase documented and modular - MeTTa test runner runs successfully with blended concept output

Milestone 3 - Final Integration Documentation & Demonstration

Description

Duration: 12 Weeks Covers packaging documentation polishing demo Community-ready output + optional review feedback 🗓️ Estimated Timeline: Week 13 to Week 24 This milestone focuses on integrating all developed modules into a cohesive framework preparing comprehensive documentation recording a demo run with concept blending examples and packaging everything for open-source release under the agreed licensing terms. Feedback from reviewers or collaborators (if any) will be incorporated here.

Deliverables

- Integrated MeTTa module suite (blending + FCA + memory) - Final PDF documentation and inline code comments - Recorded concept demonstration (video or slides) - GitHub-ready structure for partial open release - Submission-ready project bundle (ZIP)

Budget

$15,000 USD

Success Criterion

- Successful concept blending output from final system - Documentation passes peer review and test integration - Repository passes completeness checklist - Demo clearly showcases capabilities and logic flow

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The weighted average of the 4 perspectives Overall

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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

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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.

Pragadeesh

Project Owner

Lead Developer, System Architect, MeTTa Module Engineer, Documentation Author

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