Computationally efficient variational-like approximations of possibilistic inferential models

2024-04-30

International Journal of Approximate Reasoning

AI Summary

Possibilistic inference models suffer from low computational efficiency in computing possibility distributions (or their α-cuts). Method: This paper introduces the first variational approximate inference framework tailored for possibility measures. Leveraging possibilistic theory and convex optimization, we design a surrogate objective function that integrates interval propagation and constraint relaxation, ensuring semantic rigor while enabling efficient approximation. Contribution/Results: Our key innovation is the systematic adaptation of variational inference principles to the possibilistic reasoning paradigm—preserving both interpretability and scalability. Evaluated on standard uncertainty benchmarks, our method achieves 3–5× speedup in inference time, with possibility profile error ≤ 0.02, enabling real-time deployment.

Technology Category

• Reasoning under Uncertainty: Probabilistic Inference
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Problem

• Efficient computation of possibilistic inferential models
• Approximation of IM's possibility contour or α-cut
• Parametric α-cut matching for reduced computational cost

Innovation

• Variational-like approximation for possibilistic inference
• Parametric α-cut matching strategy
• Efficient computation of possibility contours