A probabilistic programming language for influence diagrams

Research output: Chapter in Book/Report/Conference proceedingsChapterpeer-review

Abstract

Probabilistic Programming (PP) extends the expressiveness and scalability of Bayesian networks via programmability. Influence Diagrams (IDs) extend Bayesian Networks with decision variables and utility functions, allowing them to model sequential decision problems. Limited-Memory IDs (LIMIDs) further allow some earlier events to be ignored or forgotten. We propose a generalisation of PP and LIMIDs called IDLP, implemented in Logic Programming and with a solver based on Reinforcement Learning and sampling. We show that IDLP can model and solve LIMIDs, and perform PP tasks including inference, finding most probable explanations, and maximum likelihood estimation.

Original languageEnglish
Title of host publicationScalable Uncertainty Management - 11th International Conference, SUM 2017, Proceedings
EditorsSerafin Moral, Daniel Sanchez, Nicolas Marin, Olivier Pivert
PublisherSpringer Verlag
Pages252-265
Number of pages14
ISBN (Print)9783319675817
DOIs
Publication statusPublished - 2017
Event11th International Conference on Scalable Uncertainty Management, SUM 2017 - Granada, Spain
Duration: 4 Oct 20176 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10564 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Scalable Uncertainty Management, SUM 2017
Country/TerritorySpain
CityGranada
Period4/10/176/10/17

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