Preprint · arXiv · July 20, 2016
Cantor meets Scott: Domain-Theoretic Foundations for Probabilistic Network Programming.
Steffen Juilf Smolka, Praveen Kumar, Nate Foster, Dexter C. Kozen, Alexandra Silva
Abstract
ProbNetKAT is a probabilistic extension of NetKAT with a denotational semantics based on Markov kernels. The language is expressive enough to generate continuous distributions, which raises the question of how to effectively compute in the language. This paper gives an alternative characterization of ProbNetKAT's semantics using domain theory, which provides the foundations needed to build a practical implementation. The new semantics demonstrates that it is possible to analyze ProbNetKAT programs precisely using approximations of fixpoints and distributions with finite support. We develop an implementation and show how to solve a variety of practical problems including characterizing the expected performance of traffic engineering schemes based on randomized routing and reasoning probabilistically about properties such as loop freedom.
More by Praveen Kumar
- Model for Long-Term Monitoring of Geological Processes in Kazakhstan Using Intelligent Drone PortsJournal of Computational and Cognitive Engineering · 2026
- Wearable Gyroscope-Based Motion Tracking and Hand Gesture Recognition Using ANOVA for Feature DiscriminationJournal of Innovative Image Processing · 2026 · Citations: 3
- FINE-TUNING SMALL LANGUAGE MODELS FOR MENTAL HEALTH TEXT CLASSIFICATION: A PARAMETER-EFFICIENT APPROACH OUTPERFORMING ZERO-SHOT LARGE LANGUAGE MODELSScientific Journal of Astana IT University · 2026
- Vision-aided online A$$^*$$ path planning for efficient and safe navigation of service robotsInternational Journal of Intelligent Robotics and Applications · 2026 · Citations: 2