RFP Details
Long description
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 advanced cognitive frameworks.
PRIMUS is a hybrid neuro-symbolic AGI architecture designed to integrate and ingest various deep neural network (DNN) architectures. Due to the self-similar structures observed at multiple spatio-temporal scales in complex systems, and informed by recent neuroscience research, this RFP aims to explore how neuro-symbolic DNNs can be embedded within the PRIMUS system. By embedding logic rules within DNNs, we seek to advance both experiential learning and/or higher-order reasoning across dynamic systems.
The focus of this RFP is to investigate the use of neuro-symbolic DNN architectures for embedding logic rules, which can be derived from two key sources:
- Experiential Learning: Rules generated by agents exploring and interacting with their environment, such as in the AIRIS system, where agents autonomously generate and adapt logic rules based on sensory inputs and actions.
- Higher-Order Reasoning: More complex logic rules provided by human users, allowing systems to engage in abstract or hierarchical reasoning tasks.
AIRIS (Autonomous Intelligent Reinforcement Interpreted Symbolism) is an AI system designed to generate and adapt symbolic rules through experiential learning. It operates by allowing agents to explore and learn about their environments, using sensory inputs to autonomously create symbolic rules that can then be applied to different contexts. This system emphasizes transparency and adaptability, where rules can be traced, interpreted, and adjusted in real-time. AIRIS is domain-agnostic, meaning the learned rules are not tied to a specific domain and can coexist across multiple environments, making it versatile for diverse AI applications.
PyNeuraLogic is a neuro-symbolic AI framework that combines differentiable logic programming with deep learning models, particularly graph neural networks (GNNs). It allows symbolic logic to be embedded into neural networks, enabling systems to perform reasoning tasks while maintaining the flexibility and scalability of DNNs. PyNeuraLogic focuses on using logic-based rules in tasks involving structured data, such as graphs, and integrates these rules with the learning capacity of neural networks. This hybrid architecture supports probabilistic reasoning and can be used for various AI tasks, including knowledge representation, graph reasoning, and decision-making
These logic rules can be embedded into diverse DNN architectures, such as GNNs, LLMs, or other neural architectures. The goal is to demonstrate how these embeddings enhance the system’s reasoning capabilities, making it possible for AI systems to operate more flexibly in dynamic environments. For example, by embedding rules from AIRIS into PyNeuraLogic, a system could process learned rules in the form of GNNs or LLMs, which would then be capable of reasoning over the outputs of these neural structures.
Kolmogorov-Arnold Networks (KANs) are networks inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). In place of fixed activation functions with learnable weights over nodes ("neurons"), KANs have learnable activation functions on edges ("weights") with a fixed unweighted sum over nodes.
This exploration will involve:
- A comprehensive survey and analysis of current neuro-symbolic DNN architectures capable of embedding logic rules. Researchers are expected to evaluate the strengths and limitations of each architecture, particularly with respect to handling dynamic system behaviors across spatio-temporal scales.
- Proof of Concept (POC). We can easily see two ideas for POCs:
- Use PyNeuraLogic to demonstrate how logic rules can be embedded within a chosen DNN architecture. For example, simple logic rules derived from AIRIS could be embedded into GNNs using PyNeuraLogic, allowing the system to reason over the outputs of those GNNs, or LLMs. This POC would show how such architectures improve both experiential learning and/or higher-order reasoning.
- As detailed in “KAN: Kolmogorov–Arnold Networks”, KANs operate quite differently than does PyNeuraLogic. KANs are quite interesting in their own right and appear to be more effective at representing certain types of functions, especially those of interest to the mathematical physics community, than do MLPs. Being built upon continuous splines, KANs can help bridge the discrete-continuous divide. They can also provide excellent human interpretability, yet sometimes rely on post-processing by either an automated symbolic regression step or, alternatively, human interaction to do so. One idea for a POC involving KANS would be to explore how well KANs could be used to ingest accurate and explainable DNNs in Hyperon. As one example, perhaps one could use KANs to create a predictive model for real-time energy consumption in a smart grid. The goal could then be to demonstrate how KANs could improve accuracy and explainability of the model, particularly in handling dynamic continuous data (e.g. fluctuating energy usage) alongside discrete events (e.g. switching energy sources). Another possibility could involve market predictions.
For all proposals, researchers are encouraged to demonstrate how their approach can drive complex system dynamics, potentially applying these methods to real-world AI tasks such as AI planning, natural language understanding, or decision-making systems.
Expected Outcomes:
- Comprehensive Survey and Evaluation: A detailed evaluation of different neuro-symbolic architectures, including PyNeuraLogic, KANs, and others, with a focus on their ability to embed logic rules and enhance reasoning capabilities.
- Architectural Comparisons: A comparison of how these architectures perform in embedding rules, addressing challenges in scalability, flexibility, and integration with complex systems across multiple spatio-temporal scales.
- Proof of Concept (POC): A POC in the MeTTa language that demonstrates the feasibility of embedding logic rules into DNNs and applying reasoning over their outputs, using systems like AIRIS or user-supplied rules.
- System Dynamics Demonstration: Evidence that the approach can drive interesting dynamics across multiple scales, showing improvements in reasoning and decision-making in dynamic environments.
- Explainability: Explain how different architectures improve human interpretability and explainability of AI systems.
- Learning from Small Data: Compare how different architectures can learn from small data using symbolic knowledge and reasoning.
- Multiparadigmality: Bridge the gap between data-driven and symbolic reasoning.
- Structured Learning: Evaluate how different architecturea can solve problems in domains in which structured learning is critical (e.g. medical ontologies).
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?
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