SingularityNET

RFP Owner

Experiment with concept blending in MeTTa

Experiment with concept blending and (incl. Fuzzy, paraconsistent) formal concept analysis in MeTTa

Type SingularityNET RFP
Total RFP Funding $100,000 USD
Proposals 1
Awarded Projects n/a

Overview

Est. Complexity
💪 75/ 100
Est. Execution Time
⏱️ 6 Months
Proposal Winners
🏆 Multiple
Funding Range
$30,000 - $60,000 USD

Tags:
AGI R&D
MeTTa

Short summary

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.

Main purpose

The purpose of this RFP is to advance the exploration of concept creation methods, such as formal concept analysis and concept blending, and their integration into the Hyperon framework. This research will contribute to developing more efficient and creative concept formation systems, essential for AGI development. Additionally, these experiments aim to push forward the capacity of Hyperon to support innovative concept synthesis mechanisms.

Long description

Context and background SingularityNET Foundation, in collaboration with other partners such as the OpenCog Foundation and TrueAGI, is working toward a scalable implementation of the Hyperon AGI framework running on decentralized infrastructure, and toward implementation of the PRIMUS cognitive architecture within this framework. Hyperon and PRIMUS are complex systems involving multiple components, which need to demonstrate appropriate functionalities both individually and in combination. This RFP aims to address a portion of this overall need, via funding the initial iteration of one significant component of PRIMUS within Hyperon. This RFP has two primary goals, both centered on the exploration of concept creation techniques within the OpenCog Hyperon system using the MeTTa programming language.

The first goal is to experiment with methods for generating new concepts based on existing concepts and data, evaluating the resulting algorithms for effectiveness and creativity. While Formal Concept Analysis (FCA) was implemented in OpenCog Classic to support these tasks, this project encourages extending FCA into more advanced frameworks such as fuzzy FCA and paraconsistent FCA. These variations allow reasoning under uncertainty and inconsistency, making them particularly valuable for more flexible, creative systems. There may also be other frameworks worth exploring beyond FCA that can be integrated into this experimentation.
The second goal is to implement one or more of these concept creation algorithms in Hyperon. MeTTa, the core language of the Hyperon architecture, provides an ideal environment for these experiments due to its flexibility and symbolic representation capabilities. Successful implementations will contribute to the broader AGI development effort, focusing on dynamic and adaptive concept formation.

In this context, creativity is a critical property to consider when evaluating new concept formation algorithms. Hyperon’s cognitive processes, which operate concurrently and cooperatively, offer multiple pathways for concept creation. These include evolutionary learning, concept blending, and formal concept analysis (potentially involving fuzzy or paraconsistent methods), alongside various other heuristics. The blending of these techniques allows Hyperon to generate novel concepts by abstracting from learned experiences and evidence. The formation of new concepts in Hyperon also ties into core memory structures. For example, long-term memory and working memory play a role in clustering and concept formation, while map formation and pattern mining help abstract high-level structures from raw data. Techniques such as concept blending, Occam’s-Razor-driven concept predicatization, and heuristic-based map formation support this dynamic process of concept generation. A key feature of Hyperon’s architecture is that the world model it constructs consists of the abstractions it has learned from specific cases via probabilistic logic networks (PLN) and concept creation algorithms. The system continuously refines its understanding through these mechanisms. Reinforcement learning (RL) methods can also contribute to this learning process, particularly in identifying explicit causal relationships within the data. These relationships may often be concrete but are also subject to abstract reasoning, further enriching the concept formation capabilities. Moreover, one promising area of research suggested for this RFP is using LLMs or multi-agent systems of LLMs to evaluate the creativity and novelty of the concepts generated. LLMs, with their vast knowledge of existing concepts, could serve as a metric for assessing whether newly generated concepts are innovative and useful, while avoiding purely random or irrelevant outputs. By framing concept creativity within these systems, new pathways for automated and meaningful concept evaluation can be explored. Ultimately, proposals should aim to integrate these blending and formal analysis techniques into Hyperon’s broader framework, driving the development of new abstractions and enhancing Hyperon’s ability to create novel, meaningful concepts. This will support ongoing AGI research by contributing to a more flexible and adaptive system capable of sophisticated cognitive functions. https://wikidocs.net/197350 Collaboration This RFP may be followed by subsequent RFPs for applications that leverage Hyperon/PRIMUS to carry out various applications, and that aim to guide Hyperon/PRIMUS systems in cognitive development toward beneficial AGI RFP Expected Outcomes:

Implementation of Concept Creation Algorithms: Successful integration of one or more formal concept analysis (FCA), fuzzy FCA, paraconsistent FCA, or concept blending algorithms within the Hyperon framework using MeTTa.
Creativity Evaluation Framework: Development of methods to evaluate the creativity and novelty of new concepts, potentially leveraging LLMs or multi-agent systems for assessment.
Demonstrated Practical Use: Concrete demonstrations of concept creation algorithms generating valuable, innovative outcomes, such as novel abstractions or knowledge structures integrated into Hyperon’s Atomspace.
Enhanced AGI Cognitive Synergy: Contributions toward cognitive synergy in Hyperon by improving concept formation processes, with practical applications in long-term and working memory systems.

Background & experience: A significant part of reviewing proposals goes into evaluating the ability of a team to execute the work. Please answer in as much detail as possible about related experience and accolades, and provide links to anything we can read such as published work, github, etc.

Functional Requirements

Must Have:

Implementation of concept blending using an information-theoretic criterion, similar to what was done in OpenCog Classic, OR the implementation of uncertain formal concept analysis (FCA) to generate new concepts.
Development of algorithms to create novel concepts based on existing data, evaluated for their novelty and coherence.
A qualitative evaluation framework to assess the generated concepts for creativity and value, focusing on human assessment of novelty and logical consistency.

Should Have:

Exploration of LLM-based evaluation, where LLMs could be configured to assess whether the generated concepts are novel and make sense (i.e., not too random), but this is not mandatory.
Parallel testing of the two proposed approaches (information-theoretic concept blending and uncertain FCA) to compare their effectiveness in concept creation.

Could Have:

The ability to use multi-agent systems of LLMs for enhanced creativity evaluation, allowing concepts to be assessed in real-time based on prior knowledge and creativity thresholds.
Integration of information-theoretic methods for refining generated concepts, balancing novelty with logical coherence in the evaluation phase.

Non-functional Requirements

Architecture:

The solution should be implemented in MeTTa and integrated into Hyperon’s Distributed Atomspace (DAS), supporting either information-theoretic concept blending or uncertain FCA for concept creation.
The system should support parallel processing, enabling the simultaneous testing and evaluation of different concept creation approaches.

Performance:

Ensure the system can handle parallel concept generation and evaluation with minimal latency, especially when using LLMs for evaluating novelty and coherence.

Reliability:

Implement basic fault tolerance to ensure concept creation and evaluation processes continue even if specific components fail, preserving progress in Atomspace.

Usability:

Provide a simple API or interface for researchers to run concept creation algorithms and evaluate results.
Include clear documentation on how to use and extend the concept generation algorithms and any potential LLM-based evaluation.

Maintainability:

Design the system to be modular and extensible, allowing future updates or changes to concept creation methods or evaluation tools without major rework.
Ensure the codebase is well-documented for ease of maintenance and further development by the research community

Main evaluation criteria

Alignment with requirements and objective

Does the proposal meet the requirements and advances the objectives of the RFP

Pre-existing R&D

Has the team previously done similar or related research or development work in other platforms / languages / contexts?

Team competence

Does the team have relevant skills?

Cost

Does the proposal offer good value for money?

Timeline

Does the proposal include a set of clearly defined milestones?
We highly recommend submitting proposals with project milestones along the lines of the following:
- Milestone 1: Submit a thorough research plan outlining and detailing the approach and work to be done. Deliverables: detailed research plan, agile breakdown of tasks with timeline, and framework design. 20% of grant
- Milestone 2: Complete initial development of the framework definitively showing implementation of the conceptual underpinnings of the RFP along with preliminary testing. Deliverables: draft implementation, initial testing results, and analysis against standard benchmarks. 40% of grant
- Milestone 3: Submit all final materials as committed to in the grant proposal. Deliverables (as applicable): final report with performance analysis, code, framework demonstration, documentation, recommendations, websites, etc. 40% of grant

Other resources

Hyperon and related AI-platforms are quickly evolving! This is a bit of a moving target, but the internal SingularityNET team will be available for help and expert advice, where needed. Also included:

SingularityNET technology links
Educational materials and resources for learning MeTTa
SingularityNET holds MeTTa study group calls every other week. Proposers are welcome to attend for support from our researchers and community.
Recurring Hyperon study group calls for community are currently being planned. These will cover MOSES, ECAN, PLN, and other key components of the OpenCog and PRIMUS Hyperon cognitive architectures.
Access to the SingularityNET World Mattermost server, with a dedicated channel for discussion and support among the RFP-winning teams and SingularityNET resources.

RFP Status

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Ends: 14 May. 2025 12:00 UTC Submit Proposal

1 proposals

Concept Blending in MeTTa

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

Soumil Rathi

Apr. 19, 2025

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