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:
- 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.
- Enhance AI Reasoning: Improve the ability of AI systems to perform higher-order reasoning and make decisions in dynamic, spatio-temporal environments.
- Improve Explainability: Provide enhanced human interpretability of AI systems by embedding symbolic logic into neural networks.
- Small-Data Learning: Enable AI systems to perform robust reasoning and learning tasks with limited data by leveraging symbolic knowledge.
- 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:
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
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Programming Languages:
- Preferably MeTTa, but Python, Rust, or C++ are acceptable.
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Performance:
- Emphasis on reasoning accuracy and explainability over real-time processing.
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Reproducibility:
- Clear documentation and datasets for experiment replication.
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Scalability:
- Ability to scale with increasing complexity in rule embedding and reasoning tasks.
Evaluation Criteria
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Alignment with Objectives:
- Proposal meets functional and non-functional requirements.
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Team Competence:
- Expertise in neuro-symbolic AI and AGI development.
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Value for Money:
- Cost-effectiveness relative to proposed outcomes.
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Defined Milestones:
- Clear and achievable milestones within the 6-month timeline.
Project Timeline and Milestones
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Phase 1: Foundation (Months 1-2)
- Develop initial neuro-symbolic integration and test basic logic embedding.
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Phase 2: Enhancement (Months 3-4)
- Build and optimize reasoning mechanisms and compare architectures.
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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.
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