Neuro-Symbolic Deep Nurl Ntwrk Arch-Exp Learn/resn

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Expert Rating 2.2
aasavravi1234
Project Owner

Neuro-Symbolic Deep Nurl Ntwrk Arch-Exp Learn/resn

Expert Rating

2.2

Overview

Our proposal explores neuro-symbolic DNN architectures like PyNeuraLogic and Kolmogorov-Arnold Networks (KANs) for enhancing experiential learning and higher-order reasoning. By embedding logic rules from systems like AIRIS or user-supplied abstract rules, we aim to improve reasoning, explainability, and small-data learning. The project includes comparative analysis, proof of concept, and real-world applications in dynamic systems and structured domains, advancing AI’s ability to reason and adapt effectively.

RFP Guidelines

Neuro-symbolic DNN architectures

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $160,000 USD
  • Proposals 9
  • Awarded Projects 2
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SingularityNET
Oct. 4, 2024

This RFP invites proposals to explore and demonstrate the use of neuro-symbolic deep neural networks (DNNs), such as PyNeuraLogic and Kolmogorov Arnold Networks (KANs), for experiential learning and/or higher-order reasoning. The goal is to investigate how these architectures can embed logic rules derived from experiential systems like AIRIS or user-supplied higher-order logic, and apply them to improve reasoning in graph neural networks (GNNs), LLMs, or other DNNs.

Proposal Description

Company Name (if applicable)

Lasavo Labs

Project details

Proposal: Neuro-Symbolic Deep Neural Network Architectures for Experiential Learning and Higher-Order Reasoning

Introduction

Our proposal seeks to advance the integration of neuro-symbolic DNN architectures such as PyNeuraLogic and Kolmogorov-Arnold Networks (KANs) into AI systems to enhance their experiential learning and higher-order reasoning capabilities. The research focuses on embedding logic rules derived from systems like AIRIS (Autonomous Intelligent Reinforcement Interpreted Symbolism) or user-defined higher-order logic into neural networks like GNNs and LLMs. This integration aims to bridge the gap between data-driven and symbolic reasoning, leading to improvements in explainability, adaptability, and structured learning across dynamic environments.


Objectives

The primary objectives of this proposal are:

  1. Embed Logic Rules into DNNs: Utilize neuro-symbolic architectures to incorporate logic rules from experiential learning systems like AIRIS or user-defined abstract rules into deep neural networks.
  2. Enhance AI Reasoning: Improve the ability of AI systems to perform higher-order reasoning and make decisions in dynamic, spatio-temporal environments.
  3. Improve Explainability: Provide enhanced human interpretability of AI systems by embedding symbolic logic into neural networks.
  4. Small-Data Learning: Enable AI systems to perform robust reasoning and learning tasks with limited data by leveraging symbolic knowledge.
  5. Real-World Applications: Demonstrate the capabilities of these neuro-symbolic systems in domains like medical ontologies, AI planning, and dynamic systems such as smart grids or market prediction.

Context and Background

The integration of symbolic reasoning with neural networks has long been a goal for advancing Artificial General Intelligence (AGI). Neuro-symbolic architectures, which combine the adaptability of deep learning with the formal reasoning capabilities of symbolic logic, provide a promising path forward.

Systems like PyNeuraLogic allow the embedding of symbolic logic into neural networks, enabling reasoning over structured data such as graphs. Similarly, KANs, inspired by the Kolmogorov-Arnold representation theorem, focus on continuous splines for handling dynamic data and bridging the discrete-continuous divide. This project leverages these architectures to create AI systems that are more robust, explainable, and capable of reasoning across multiple spatio-temporal scales.


Key Features of the Proposal

1. Embedding Rules from Experiential Learning (AIRIS):
AIRIS generates symbolic rules by allowing agents to explore and adapt to their environment. These rules can be embedded into DNNs like PyNeuraLogic to enhance adaptability and reasoning in dynamic systems.

2. Higher-Order Reasoning with User-Defined Logic:
User-defined logic, which includes abstract and hierarchical rules, will be integrated into architectures like KANs to enable AI systems to solve complex reasoning tasks.

3. Dynamic System Reasoning:
The project will focus on reasoning capabilities across spatio-temporal scales, improving adaptability and decision-making in environments with both continuous and discrete events.

4. Comparative Analysis:
A detailed evaluation of neuro-symbolic architectures will identify their strengths and limitations for experiential learning and higher-order reasoning.

5. Proof of Concept (POC):
The POC will demonstrate the practical application of these architectures, such as embedding AIRIS-generated rules into PyNeuraLogic for reasoning in GNNs or using KANs for real-time energy consumption modeling in smart grids.


Methodology

1. Neuro-Symbolic Integration:

  • For Experiential Learning:

    • Logic rules generated by AIRIS will be embedded into GNNs using PyNeuraLogic.
    • These rules will evolve over time based on agent-environment interactions, improving adaptability.
  • For Higher-Order Reasoning:

    • User-defined rules will be implemented in KANs to enable hierarchical reasoning and decision-making.

2. Proof of Concept:

  • Demonstrate reasoning improvements in GNNs or LLMs by embedding rules using PyNeuraLogic.
  • Use KANs to model dynamic systems, such as energy grids, demonstrating accuracy and explainability.

3. Comparative Analysis:

  • Evaluate architectures like PyNeuraLogic and KANs for rule embedding, reasoning performance, and scalability.
  • Highlight the trade-offs between experiential learning and higher-order reasoning approaches.

4. Testing and Benchmarking:

  • Test AI systems in real-world scenarios like medical ontology reasoning, smart grid energy prediction, or market analysis.
  • Benchmark results against traditional deep learning approaches to demonstrate efficiency and effectiveness.

Expected Outcomes

1. Comprehensive Survey and Evaluation:

  • Detailed analysis of neuro-symbolic architectures for embedding logic rules and reasoning enhancement.

2. Improved Reasoning Capabilities:

  • Demonstrate enhanced reasoning performance in AI systems with embedded symbolic logic.

3. Explainability and Interpretability:

  • Show how neuro-symbolic approaches improve the transparency of AI systems, making them easier to interpret.

4. Real-World Applications:

  • Successfully apply these architectures to domains requiring structured learning, such as medical ontologies or dynamic decision-making systems.

5. Published Research and Open-Source Tools:

  • Share findings through peer-reviewed publications and open-source codebases for community use.

Functional Requirements

Must Have:

  • Integration of neuro-symbolic architectures like PyNeuraLogic or KANs with systems like AIRIS.
  • Embedding of logic rules into GNNs, LLMs, or other DNNs.
  • Proof of concept demonstrating reasoning improvements.

Should Have:

  • Comparative analysis of architectures for experiential learning and higher-order reasoning.
  • Demonstration of dynamic system reasoning across spatio-temporal scales.

Could Have:

  • Hybrid architectures combining experiential learning and higher-order reasoning approaches.

Non-Functional Requirements

  1. Programming Languages:

    • Preferably MeTTa, but Python, Rust, or C++ are acceptable.
  2. Performance:

    • Emphasis on reasoning accuracy and explainability over real-time processing.
  3. Reproducibility:

    • Clear documentation and datasets for experiment replication.
  4. Scalability:

    • Ability to scale with increasing complexity in rule embedding and reasoning tasks.

Evaluation Criteria

  1. Alignment with Objectives:

    • Proposal meets functional and non-functional requirements.
  2. Team Competence:

    • Expertise in neuro-symbolic AI and AGI development.
  3. Value for Money:

    • Cost-effectiveness relative to proposed outcomes.
  4. Defined Milestones:

    • Clear and achievable milestones within the 6-month timeline.

Project Timeline and Milestones

  1. Phase 1: Foundation (Months 1-2)

    • Develop initial neuro-symbolic integration and test basic logic embedding.
  2. Phase 2: Enhancement (Months 3-4)

    • Build and optimize reasoning mechanisms and compare architectures.
  3. Phase 3: Validation (Months 5-6)

    • Validate through real-world testing and benchmarking.

Budget Breakdown

  • Personnel: $100,000
  • Computing Resources: $40,000
  • Miscellaneous: $20,000
  • Total: $160,000

Conclusion

This proposal aims to advance neuro-symbolic AI by leveraging architectures like PyNeuraLogic and KANs for embedding logic rules into DNNs. By enhancing reasoning capabilities, improving explainability, and enabling small-data learning, the project aligns with SingularityNET's vision for advancing AGI systems. The outcomes will provide robust, adaptable AI systems capable of solving complex real-world problems.

Links and references

 

Links and References:

  1. MOSES Framework: https://wiki.opencog.org/w/MOSES
  2. PyNeuraLogic GitHub: https://github.com/pkliczewski/PyNeuraLogic
  3. AIRIS GitHub: https://github.com/singularitynet/airis
  4. Kolmogorov-Arnold Networks: https://arxiv.org/abs/2006.05292
  5. Neuro-Symbolic AI: https://arxiv.org/abs/2003.00330
  6. Hyperon Overview: https://opencog.org/hyperon/

Additional videos

Proposal Video

Not Avaliable Yet

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

  • Total Milestones

    3

  • Total Budget

    $80,000 USD

  • Last Updated

    7 Dec 2024

Milestone 1 - Neuro-Symbolic Integration Foundation

Description

Develop foundational components for integrating logic rules from AIRIS or higher-order reasoning into neuro-symbolic DNNs like PyNeuraLogic and KANs. This includes initial architecture design, logic rule embedding, and basic compatibility with frameworks like GNNs and LLMs.

Deliverables

Initial neuro-symbolic architecture. Embedded basic rules for experiential learning or higher-order reasoning. Documentation for system architecture and integration processes.

Budget

$20,000 USD

Success Criterion

Successful embedding of basic symbolic logic rules into PyNeuraLogic or KANs, demonstrated by reasoning tests achieving >70% accuracy on predefined datasets.

Milestone 2 - Advanced Rule Embedding and Optimization

Description

Enhance rule embedding for dynamic systems by refining integration processes and optimizing logic processing. Includes creating mechanisms for real-time reasoning and adaptability for both experiential learning and abstract hierarchical logic tasks.

Deliverables

Optimized rule embedding methods. Tools for reasoning over dynamic systems. Comparative analysis of experiential learning vs. higher-order reasoning approaches.

Budget

$30,000 USD

Success Criterion

Demonstrated reasoning improvements with a 20% reduction in computation costs and enhanced adaptability across multiple test scenarios.

Milestone 3 - Validation and Real-World Application

Description

Conduct validation and benchmarking of the integrated neuro-symbolic DNN systems in real-world applications, such as medical ontology reasoning or dynamic decision-making in smart grids. Publish results and refine based on findings.

Deliverables

Benchmarking reports and performance comparisons. Reproducible codebase with detailed documentation. Research paper submission to peer-reviewed venues.

Budget

$30,000 USD

Success Criterion

Validation shows >30% improvement in reasoning efficiency and explainability, with results reproduced and independently verified by external researchers.

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

2.2

  • Feasibility 3.2
  • Desirabilty 2.2
  • Usefulness 2.0
  • Expert Review 1

    Overall

    1.0

    • Compliance with RFP requirements 1.0
    • Solution details and team expertise 1.0
    • Value for money 1.0
    Lack of details and questionable teams expertise.

  • Expert Review 2

    Overall

    3.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 3.0
    • Value for money 3.0
    Echos RFP requirements but lacks detail

    Needs more detailed technical and implementation plans, clearer milestones, and information on team.

  • Expert Review 3

    Overall

    2.0

    • Compliance with RFP requirements 2.0
    • Solution details and team expertise 2.0
    • Value for money 2.0
    Another rule embedding approach

    "Our proposal explores neuro-symbolic DNN architectures like PyNeuraLogic and Kolmogorov-Arnold Networks (KANs) for enhancing experiential learning and higher-order reasoning." These are not neuro-symbolic DNN architectures. However the project plan at least looks sound with analyzing how logic rules can be embedded into neural networks. That's something we have to figure out, but this project proposal does not contain new ideas how to achieve it but reads more like a wish list. However it could be tractable, hence 2 stars.

  • Expert Review 4

    Overall

    3.0

    • Compliance with RFP requirements 4.0
    • Solution details and team expertise 3.0
    • Value for money 2.0
    Relies-on/promises some major innovations but without a clear explanation for how to achieve them. Very interesting ideas though.

    The proposal seems to rely on embedding rules in various sorts of NNs including KANs, but I'm not sure how this is actually going to be done. There's a bit of "and then a miracle occurs" here. Also just embedding rules in networks, even if you figure out a nice way to do it, isn't helpful if it's not done in a way that lets the NNs generalize and combine those rules adaptively, which isn't really addressed. The proposal gives an impression of research naivete' .. which may be a wrong impression, but to overcome the impression would take more scientific detail (or relevant references etc.) than is given...

  • Expert Review 5

    Overall

    2.0

    • Compliance with RFP requirements 4.0
    • Solution details and team expertise 2.0
    • Value for money 2.0

    Solid, detailed proposal based upon creating and using symbolic logic rules. Could be more creative.

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