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 project has two goals: 

• To study how Probabilistic Logic Networks (PLN) inference control can be guided by causal learning, including causal networks; 
• To study how PLN rules can be used to learn causal versus correlational relationships. 

Proposals can address either of these goals individually or explore both and the feedback between them. The ultimate aim is to improve PLN’s ability to manage uncertain and incomplete knowledge by incorporating causal reasoning, leading to more robust and accurate inference control.

1. Guiding PLN Inference with Causal Networks:
   This thread seeks to determine how causal networks can be used to steer PLN's inference control in a specific domain or context. This may involve running large-scale inferences across domains (such as bioinformatics, genomics, or even simulated environments like Minecraft) and evaluating whether causal network-enhanced inference outperforms PLN operating independently.
2. Distinguishing Causal from Correlational Relationships:
   This line of inquiry focuses on how PLN rules can be used to learn and differentiate causal relationships from mere correlations. This is critical in fields where distinguishing between cause and effect is essential, such as scientific research, AI planning, or healthcare. Proposals should examine how PLN can recognize causality versus correlation and validate this through examples where such distinctions improve decision-making accuracy.

Researchers may consider a variety of causal network learning systems for integration with PLN. Examples include Bayesian Causal Networks, Pearl’s Do-Calculus, Structural Causal Models, Potential Outcome Framework, and Probabilistic Event Calculus. These systems offer powerful tools for reasoning about causality and could enhance PLN's capabilities in distinguishing causal relationships from correlative ones. Additionally, Information Theoretic approaches can provide insights into the relationships between variables, helping PLN refine its inference control or causal learning. Deep Neural Network approaches also appear promising.

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:

• For PLN Inference Control: Demonstrate how integrating causal networks enhances PLN’s ability to guide inference control within a chosen domain. Provide examples where this improves reasoning accuracy or decision-making.
• For Causal vs. Correlational Learning: Develop methods that use PLN rules to distinguish between causal and correlational relationships. Offer concrete examples and validation metrics showing how this distinction leads to better outcomes in a given context.
• Test Suite and Evaluation: Whether focusing on one of the above or both, provide a comprehensive set of test cases and evaluation metrics that measure performance improvements.
• Domain-specific Application: Apply the chosen methodology (inference control, causal learning, or both) to a practical domain (e.g., bioinformatics, AI planning, or simulated environments like Minecraft), demonstrating clear improvements in causal reasoning or inference accuracy.