Innovative Concept Blending in MeTTa

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Expert Rating 3.5
Anthony Oliko
Project Owner

Innovative Concept Blending in MeTTa

Expert Rating

3.5

Overview

We aim to implement and experiment with concept blending and advanced Formal Concept Analysis (FCA), including fuzzy and paraconsistent variations, within the MeTTa programming language of the Hyperon framework. By integrating these methods, we aim to generate novel, creative concepts, evaluate their creativity using LLM-based frameworks, and enhance the Hyperon system's cognitive synergy, contributing to AGI development.

RFP Guidelines

Experiment with concept blending in MeTTa

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $100,000 USD
  • Proposals 6
  • Awarded Projects n/a
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SingularityNET
Oct. 4, 2024

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.

Proposal Description

Company Name (if applicable)

Trenches AI

Project details

Our proposed research focuses on enhancing the OpenCog Hyperon framework's capability to perform creative and efficient concept formation by integrating advanced concept blending techniques and Formal Concept Analysis (FCA), including its fuzzy and paraconsistent variations. These methods will be implemented in the MeTTa programming language and evaluated for their ability to generate novel and coherent concepts. This project aligns with the broader objectives of developing cognitive architectures essential for Artificial General Intelligence (AGI).

Project Objectives

  1. Implementation of Concept Blending Techniques:
    Building upon information-theoretic approaches used in OpenCog Classic, we aim to develop algorithms that blend existing concepts into innovative abstractions. These will leverage the flexibility of MeTTa and Hyperon’s Atomspace, enabling the generation of novel, meaningful structures.

  2. Advanced Formal Concept Analysis (FCA):
    Extending traditional FCA into fuzzy and paraconsistent domains allows reasoning under uncertainty and inconsistency. These variations will be tested for their ability to support flexible and adaptive concept formation, crucial for AGI.

  3. Creativity and Novelty Evaluation:
    The proposal emphasizes developing frameworks for assessing the creativity of generated concepts. Large Language Models (LLMs) or multi-agent systems of LLMs will be utilized to evaluate whether the generated concepts are both innovative and practically useful.

  4. Integration into Hyperon:
    The final goal is the seamless integration of these methodologies into Hyperon’s cognitive architecture, contributing to its cognitive synergy and supporting ongoing AGI development.


Proposed Work

Phase 1: Research and Design

  1. Algorithm Selection:
    Identify existing algorithms for information-theoretic concept blending and FCA that can be adapted for use in Hyperon.
  2. Framework Design:
    Define a modular design for implementing the selected methods in MeTTa, ensuring compatibility with Hyperon’s Atomspace and distributed architecture.

Phase 2: Implementation

  1. Concept Blending:
    Develop and test blending algorithms in MeTTa, focusing on information-theoretic criteria to optimize novelty and coherence.
  2. Fuzzy and Paraconsistent FCA:
    Extend traditional FCA algorithms to incorporate fuzzy sets and paraconsistent logic, enabling reasoning under uncertainty and inconsistency.
  3. Integration into Hyperon:
    Implement the developed algorithms within Hyperon’s Distributed Atomspace (DAS), ensuring they work synergistically with other cognitive processes.

Phase 3: Evaluation

  1. Creativity Assessment:
    Use LLMs or multi-agent LLM systems to evaluate the novelty and coherence of generated concepts, avoiding purely random outputs.
  2. Performance Testing:
    Assess the efficiency and scalability of the implemented methods, particularly in scenarios involving large-scale data or concurrent processes.
  3. Comparative Analysis:
    Compare the effectiveness of concept blending and fuzzy/paraconsistent FCA in generating novel, valuable concepts.

Phase 4: Demonstration and Documentation

  1. Practical Demonstrations:
    Showcase examples of novel abstractions or knowledge structures generated by the system, illustrating its potential applications in AGI.
  2. Documentation:
    Provide comprehensive documentation of the algorithms, their implementation, and the evaluation framework, facilitating further research and development.

Key Innovations

  1. Blending Information-Theoretic and Logical Approaches:
    The project uniquely combines information-theoretic concept blending with fuzzy and paraconsistent FCA, creating a hybrid approach to concept formation.
  2. Creativity Evaluation with LLMs:
    Leveraging LLMs for evaluating novelty and creativity introduces a novel, automated metric for assessing the quality of generated concepts.
  3. Integration into Hyperon:
    The work directly contributes to the evolving Hyperon framework, ensuring practical utility and alignment with AGI development goals.

Expected Outcomes

  1. Implementation of Concept Creation Algorithms:
    Functional implementations of concept blending and fuzzy/paraconsistent FCA in MeTTa, integrated into Hyperon.
  2. Evaluation Framework:
    A robust method for assessing the creativity and coherence of generated concepts, including potential LLM-based tools.
  3. Demonstrated Utility:
    Practical examples of novel concepts and abstractions generated by the system, illustrating their relevance and applicability to AGI.
  4. Enhanced Cognitive Synergy:
    Contributions to Hyperon’s cognitive architecture, supporting its broader goal of scalable, decentralized AGI.

Potential Applications

The methodologies developed in this project can be applied across various domains, including:

  • Knowledge Discovery: Generating new insights from complex data.
  • Creative AI: Supporting automated design, art, and innovation.
  • Scientific Research: Facilitating hypothesis generation and testing.

Open Source Licensing

Apache License

This project will be released under the Apache License 2.0, a permissive open-source license that allows anyone to use, modify, and distribute the code and data, provided that appropriate credit is given to the original authors.

Components Outside This License

At this stage, there are no planned components or resources in the project that fall outside the scope of the Apache License 2.0. However, the project will rely on external tools and libraries (e.g., packages, data processing utilities) that may be governed by their respective licenses. In such cases:

  • All dependencies and third-party resources will be clearly documented, along with their licenses.
  • Care will be taken to ensure compatibility between the Apache License 2.0 and any third-party licenses.

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

  • Total Milestones

    5

  • Total Budget

    $45,000 USD

  • Last Updated

    8 Dec 2024

Milestone 1 - Research and Framework Design

Description

Conduct foundational research on concept blending and advanced Formal Concept Analysis (FCA) including fuzzy and paraconsistent variations. This involves reviewing relevant literature analyzing existing implementations in OpenCog Classic and defining the scope and requirements for integrating these techniques into Hyperon. Design a modular framework for implementation in MeTTa ensuring compatibility with Hyperon’s Atomspace and cognitive architecture.

Deliverables

1. A detailed technical report outlining the research findings and proposed algorithms for concept blending and FCA. 2. A modular framework design document specifying the architecture integration points and planned implementation phases. 3. A comprehensive timeline for subsequent milestones ensuring alignment with project objectives.

Budget

$8,000 USD

Success Criterion

1. Completion of a technical report and framework design document reviewed and approved by key stakeholders. 2. Clear, actionable implementation plans for subsequent milestones.

Milestone 2 - Implementation of Concept Blending

Description

Develop and implement information-theoretic concept blending algorithms in MeTTa. The implementation will focus on combining existing concepts into innovative abstractions while balancing novelty and coherence. Algorithms will be optimized for integration with Hyperon’s Distributed Atomspace (DAS).

Deliverables

1. Fully implemented and documented concept blending algorithms in MeTTa. 2. Integration of these algorithms into Hyperon including test cases to validate their functionality. 3. Initial results demonstrating basic concept blending capabilities.

Budget

$12,000 USD

Success Criterion

1. Successful execution of blending algorithms in MeTTa, with verified functionality through test cases. 2. Demonstration of the ability to generate meaningful and novel concept abstractions.

Milestone 3 - Advanced FCA Implementation

Description

Extend traditional FCA into fuzzy and paraconsistent domains implementing algorithms in MeTTa. These techniques will enable reasoning under uncertainty and inconsistency enhancing Hyperon’s flexibility in concept formation. Algorithms will be tested for scalability and compatibility with the Atomspace.

Deliverables

1. Implemented fuzzy and paraconsistent FCA algorithms in MeTTa. 2. Test results showcasing the system’s ability to handle uncertain and inconsistent data during concept formation. 3. Documentation of the algorithms their theoretical basis and implementation details.

Budget

$12,000 USD

Success Criterion

1. Verified performance of fuzzy and paraconsistent FCA algorithms through comprehensive testing. 2. Successful integration into Hyperon, demonstrating improved flexibility and reasoning capabilities.

Milestone 4 - Creativity Evaluation Framework

Description

Develop a creativity evaluation framework for assessing the novelty and coherence of generated concepts. The framework will include LLM-based tools or multi-agent systems to measure creativity supported by human validation to ensure alignment with project goals.

Deliverables

1. Implemented LLM-based evaluation tools for automated creativity assessment. 2. Human evaluation protocols for validating the novelty and logical consistency of generated concepts. 3. A detailed report on the evaluation process and findings including recommendations for improvement.

Budget

$8,000 USD

Success Criterion

1. Demonstration of the creativity evaluation framework in action, with consistent results across test cases. 2. Positive feedback from stakeholders on the reliability and utility of the evaluation methods.

Milestone 5 - Demonstration and Final Report

Description

Conduct a comprehensive demonstration of the implemented concept blending and FCA algorithms showcasing their ability to generate novel and meaningful concepts. Prepare a final report summarizing the project outcomes challenges and potential next steps for further research.

Deliverables

1. A live or recorded demonstration of the system generating and evaluating concepts. 2. Final report detailing the project’s results including performance metrics key findings and lessons learned. 3. Recommendations for future work to advance Hyperon’s concept formation capabilities.

Budget

$5,000 USD

Success Criterion

1. Successful demonstration of the system to stakeholders, with clear evidence of its ability to meet project objectives. 2. Approval of the final report by the project’s review committee.

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

3.5

  • Feasibility 4.0
  • Desirabilty 3.5
  • Usefulness 3.5
  • Expert Review 1

    Overall

    4.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 4.0
    • Value for money 4.0
    This is a competent proposal that covers all the bases and demonstrates a genuine understanding of the topic area.

  • Expert Review 2

    Overall

    3.0

    • Compliance with RFP requirements 3.0
    • Solution details and team expertise 3.0
    • Value for money 3.0

    Vague and not enough detailed information regarding algorithms.

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