evanluoicecream
Project OwnerMain researcher
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.
Implementing causal, versus strictly correlative, inference can lead to a deeper understanding of many complex problems. Probabilistic Logic Networks (PLN), with their robust mathematical foundations for handling uncertain and incomplete knowledge—such as induction, abduction, analogy, and reasoning about time—offer a strong framework for this task. This project has two main goals:
Proposals can address either of these goals separately or explore both together and the feedback between them. Submissions should include testable examples to validate the approach and demonstrate how causal reasoning improves PLN’s overall performance.
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Conduct literature review on Bayesian networks, Pearl’s Do-Calculus, and Structural Causal Models (SCMs). Define project objectives and specify goals for the Causal Inference Engine (CIE), including functional requirements and targeted applications (e.g., bioinformatics, AI planning). Develop initial theoretical framework for integrating causal networks with PLN for inference control.
A report for the literature review containing the produced work.
$5,000 USD
Identify datasets relevant to bioinformatics and AI planning domains. Establish data pipelines for data preprocessing and feature engineering, focusing on variables suited to causal inference. Construct preliminary Bayesian network structures based on domain knowledge.
A sample of the dataset.
$10,000 USD
Preliminary Bayesian network models structured from collected datasets, including initial causal relationships. Algorithm documentation for structural learning techniques used (e.g., PC, GES). Parameter estimation report with results from maximum likelihood or Bayesian estimation.
Example code
$20,000 USD
Evaluation report documenting findings from pilot testing of Bayesian networks, including validation results and feedback from experts. Refined Bayesian network models based on pilot test outcomes. Project summary report covering foundational development and initial network validations.
The evaluation report.
$10,000 USD
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