evanluoicecream
project owner
Bayesian Causal Networks for Probabilistic Logic
This research aims to improve Probabilistic Logic Networks (PLN) by integrating causal inference mechanisms to improve differentiation between causation and correlation. The project focuses on two primary goals: guiding PLN inference control using causal networks and exploring PLN’s capability to learn causal relationships. By employing causal learning systems, such as Bayesian networks and Pearl’s Do-Calculus, the research seeks to validate enhanced reasoning accuracy in domains like bioinformatics and AI planning. In sum, this work will refine decision-making processes in AI, leading to more robust and accurate predictions in complex environments.
khellar
Dec 2, 2024 | 1:53 PMEdit Comment
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Hi Dominik this work request is not meant to produce a service for our marketplace. Apologies for the confusion. Please disregard!
Dominik Tilman
Dec 2, 2024 | 1:35 PMEdit Comment
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@khellar @admin. The submission template for this RFP asks for a description of a service to be integrated into the SNET Marketplace. However, it is not clear from the RFP description what this should be (there is only the integration with Hyperon mentioned). Is this a mistake (because I don't see it in other RFPs either), or do I misunderstand it?