Teaching AGI to Reason via Neuro-Symbolic Learning

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Expert Rating 3.0
JacobBillings
Project Owner

Teaching AGI to Reason via Neuro-Symbolic Learning

Expert Rating

3.0

Overview

This proposal presents an approach to machine cognition using PyNeuraLogic in an unsupervised neural-symbolic architecture. The core innovation treats knowledge graphs as dense weighted networks, enabling the discovery of latent logical relationships between entities. The implementation follows two stages: template bootstrapping with established knowledge bases, followed by batched introduction of new entities. Through message passing and weight updates, the system infers potential relationships beyond explicit definitions. The research concludes by evaluating how this template enhances Large Language Model reasoning compared to standard retrieval augmented generation techniques.

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

Project details

## Background and Problem Statement

The PRIMUS cognitive architecture requires enhanced reasoning capabilities to bridge the gap between symbolic logic and neural learning. Current approaches face several critical challenges: limited ability to discover implicit relationships in knowledge bases, difficulty in generalizing from sparse data, and barriers to integration between symbolic reasoning and neural architectures. This proposal addresses these challenges through a novel application of PyNeuraLogic, enabling both experiential learning and higher-order reasoning within a unified framework.

## Research Objectives

My research aims to develop a neuro-symbolic learning system that enhances PRIMUS's reasoning capabilities through:

- Implementation of a dense network representation for knowledge graphs using PyNeuraLogic
- Development of a dynamic ontology expansion system supporting probabilistic relationship inference 
- Creation of a template-based augmentation system for improved LLM reasoning
- Establishment of interpretable logic programming foundations for transparent decision-making

## Core Architecture Overview

The system architecture leverages PyNeuraLogic to implement a differentiable graph neural network that treats knowledge graphs as dense weighted networks. This approach enables the discovery of latent logical relationships between entities through sophisticated message passing and weight updates.

The implementation transforms traditional sparse knowledge graphs into dense weighted networks where relationships between entities are represented as continuous-valued connections. The network maintains a learned tensor where dimensions correspond to entities and tensor values represent probabilistic relationship strengths. These relationships exist along a continuous spectrum, allowing the system to represent uncertainty and degrees of relationship strength.

### Message Passing Implementation 

The message passing cycle operates through four distinct stages that together enable complex logical pattern discovery:

1. Message Generation: The system generates messages at each node containing information about its current state and weighted relationships. These messages are transmitted across predicate edges, incorporating both source node state and predicate weights.

2. Message Aggregation: Each receiving node collects incoming messages through an aggregation function that combines multiple inputs. The system implements both sum and mean aggregation functions based on relationship types.

3. Node State Update: Nodes update internal states using their previous state and aggregated messages, implementing a differentiable form of memory that maintains historical information while incorporating new relationship data.

4. Network Readout: After multiple message passing iterations, the system produces a final readout capturing both explicit and discovered implicit relationships.

### Weight Update Process

The weight update mechanism implements a sophisticated learning process through:

- Forward pass maintaining a computational graph tracking information flow
- Loss computation comparing outputs against known relationships and logical constraints
- Optimized backpropagation calculating gradients for predicate weights
- Adaptive optimization with regularization to prevent overfitting

### Batch Processing System

The implementation includes an efficient batch processing system for large-scale knowledge graphs, featuring:

- Representative batch construction ensuring important composite relationships appear together
- Multi-epoch training to refine relationship understanding across contexts
- Two-stage entity introduction process for contextualizing and integrating new entities

## Integration with Large Language Models

The LLM integration architecture implements a bidirectional interface through:

- Structured parsing for query-to-predicate conversion
- Probabilistic reasoning with learned templates
- Template-augmented response generation maintaining natural language fluency

## Validation Architecture

My validation system implements comprehensive testing across:

- Link prediction using MRR and Hits@k metrics
- Knowledge base completion on FB15k-237 and WN18RR datasets
- Controls preventing artificial performance inflation

## Implementation Plan and Timeline

### Phase 1: Core Architecture (Month 1-2)
- PyNeuraLogic implementation for dense knowledge graphs
- Message passing and weight update mechanisms
- Comprehensive testing and documentation

### Phase 2: Knowledge Base Integration (Month 3)
- Integration with major knowledge bases
- Entity introduction and batch training implementation
- Performance benchmarking

### Phase 3: Template Learning Validation (Month 4)
- Validation framework implementation
- Higher-order pattern discovery analysis
- Benchmark comparisons

### Phase 4: LLM Integration (Month 5-6)
- Bidirectional interface development
- Template-based reasoning enhancement
- Comparative analysis and system demonstration

## Validation Methodology

My validation strategy encompasses:

Technical Performance:
- Link prediction accuracy metrics
- Knowledge base completion benchmarks
- System scalability assessment

Integration Success:
- RAG system comparisons
- Template generalization measurements
- Reasoning accuracy improvements

## Expected Outcomes

This work will deliver:
- PyNeuraLogic implementation for dense knowledge graph learning
- Extensible template learning system
- Novel LLM integration framework
- Empirical validation of dense network advantages
- Comprehensive documentation

## Resource Requirements

The project requires:
- My development expertise neural networks, with online learning of PyNeuraLogic
- High-performance computing resources (existing bare-metal server + $4000 in upgrades and potential remote function calls)
- Benchmark datasets
- Testing environment and documentation systems

## Risk Management

Technical Risks:
- Scalability challenges: Mitigated through efficient batching
- Integration complexity: Addressed via comprehensive testing
- Performance bottlenecks: Managed through continuous optimization

Research Risks:
- Limited improvements: Addressed through iterative refinement
- LLM integration challenges: Mitigated via staged testing
- Dataset issues: Managed through diverse data sources

## Conclusion

This proposal presents a structured approach to enhancing PRIMUS's cognitive architecture through neuro-symbolic learning. My implementation strategy, milestone structure, and validation methodology position this research to deliver significant improvements in AI reasoning capabilities while maintaining system interpretability and scalability.

Open Source Licensing

LGPL - Lesser General Public License

Proprietary knowledge bases and/or datasets used for training are excluded from the LGPL license. 

Any configuration files containing sensitive parameters, API keys, or implementation-specific details are excluded from the LGPL license.

Any application specific implementations of the template learning system for particular will not be released under this license.

Links and references

Academic research: https://orcid.org/0000-0002-8186-6126

Public repositories: https://github.com/JacobCWBillings

Proposal Video

Not Avaliable Yet

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

  • Total Milestones

    4

  • Total Budget

    $28,000 USD

  • Last Updated

    8 Dec 2024

Milestone 1 - PyNeuraLogic Template Learning Framework

Description

Establish the foundational architecture for template learning using PyNeuraLogic. This phase focuses on implementing the core message passing and weight update mechanisms within the GNN framework. The implementation will include the basic infrastructure for treating knowledge graphs as dense weighted networks setting up the computational pipeline for parallel message passing and developing the initial weight update process.

Deliverables

- Documented PyNeuraLogic implementation of the dense knowledge graph architecture - Message passing cycle implementation with parallel processing capability - Weight update process implementation with gradient tracking - Unit tests covering core functionality - Technical documentation detailing system architecture and component interaction - Code repository with version control and documentation

Budget

$7,000 USD

Success Criterion

- Successful implementation of message passing mechanism with demonstrable convergence - Weight update process showing stable learning behavior - System successfully processes small-scale knowledge graphs - All unit tests passing with >90% code coverage - Technical documentation peer-reviewed and approved - Code review completed with all critical issues resolved

Milestone 2 - Ontology Bootstrap System

Description

Develop and implement the ontology bootstrapping system using established knowledge bases. This phase involves creating interfaces to major knowledge bases (DBpedia NELL SingularityNET KB) implementing the entity introduction mechanism and establishing the batched training process for new entity integration.

Deliverables

- Knowledge base integration interfaces - Entity introduction mechanism implementation - Batched training process for new entities - Integration tests for knowledge base connections - Documentation of knowledge base integration protocols - Performance benchmarks for entity introduction

Budget

$5,600 USD

Success Criterion

- Successful integration with at least three major knowledge bases - Entity introduction mechanism demonstrating consistent performance - Batched training process showing stable learning curves - Integration tests passing with >90% success rate - Documentation verified by independent technical review - Performance benchmarks meeting or exceeding baseline metrics

Milestone 3 - Template Learning Performance Analysis

Description

Implement and execute comprehensive validation procedures for the template learning system. This phase focuses on link-prediction benchmarks knowledge base completion tasks and systematic evaluation of the system's ability to discover higher-order logical patterns.

Deliverables

- Implementation of validation framework - Benchmark results on FB15k-237 and WN18RR datasets - Analysis of higher-order logical pattern discovery - Validation test suite - Comprehensive performance analysis report - Validation methodology documentation

Budget

$5,600 USD

Success Criterion

- Mean Reciprocal Rank (MRR) and Hits@k metrics exceeding baseline models - Successful completion of knowledge base completion tasks - Demonstrated discovery of non-trivial higher-order logical patterns - Validation tests showing reproducible results - Performance analysis showing statistical significance - Independent verification of validation methodology

Milestone 4 - LLM-Template Integration System

Description

Develop and implement the bidirectional interface between the template learning system and Large Language Models. This phase involves creating the RAG-like query system implementing the logical predicate conversion mechanism and developing the template-based reasoning enhancement system.

Deliverables

- LLM integration interface - Query-to-predicate conversion system - Template-based reasoning enhancement implementation - Comparative analysis versus standard RAG systems - Integration testing suite - System performance documentation - End-to-end demonstration system

Budget

$9,800 USD

Success Criterion

- Successful bidirectional communication between LLM and template system - Accurate conversion of natural language queries to logical predicates - Demonstrable improvement in semantic reasoning capabilities - Comparative analysis showing advantages over standard RAG - All integration tests passing - System performance meeting or exceeding specified requirements - Successful demonstration of end-to-end system functionality

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Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

3.0

  • Feasibility 4.0
  • Desirabilty 3.6
  • Usefulness 3.0
  • Expert Review 1

    Overall

    2.0

    • Compliance with RFP requirements 3.0
    • Solution details and team expertise 5.0
    • Value for money 2.0
    Benifit is to explore PyNeuralLogic, but proposal does not state any details how it will help or integrate with PRIMUS components

    Proposal does not state any details how it will help or integrate with PRIMUS components or even how the logic encoding will be done. On the plus side that someone will explore PyNeuralLogic, however it is very questionable how this research will correlate or integrate with our semantic reasoners. On the plus side, researcher request only $28k what makes it cheapest proposal for given RFP.

  • Expert Review 2

    Overall

    5.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 5.0
    • Value for money 5.0
    Excellent

    Very good proposal. Enhances AGI reasoning via dense knowledge graphs in PyNeuraLogic. Enables latent relationship discovery and template-based LLM reasoning. Aligns with RFP goals. Scalable, phased plan. Validation on benchmarks. Credible team.

  • Expert Review 3

    Overall

    3.0

    • Compliance with RFP requirements 3.0
    • Solution details and team expertise 3.0
    • Value for money 3.0
    Knowledge graph as dense graph neural nets with message passing

    Knowledge graph as dense graph neural networks, updated via message passing. Interesting to use PyNeuralLogic in this way, this sounds potentially promising and could lead to novel ways of handling knowledge bases in a way that can use Deep Learning machinery while handling symbolic information. I will give 3 stars as I am optimistic the author will succeed in encoding knowledge bases into GNN even though details were lacking in the proposal. Additionally the author will face the challenge of knowledge updating which is especially difficult with DNN approaches. However it is not impossible, the relevant issue and potential handling is discussed in Dohare, S., Hernandez-Garcia, J. F., Lan, Q., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2024). Loss of plasticity in deep continual learning. Nature, 632(8026), 768-774.

  • Expert Review 4

    Overall

    3.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 3.0
    • Value for money 3.0
    A lot of interesting stuff here, and clearly this is an expert proposer, but the novel ideas are presented in a somewhat confusing/unclear way

    I wanted to love this proposal because there are clearly a lot of creative ideas here ,but they way they're explained I found it hard to understand if they fully make sense or not... Key terms are not sufficiently specified or defined, and it's not made really clear how the neural learning and the KG interoperate... It may just be a problem of trying to get across a lot of complex novel stuff in the short space of a brief proposal, but this could be addressed by linking to auxiliary information....

  • 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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