From Probabilistic Programming to Complexity-based Programming
Résumé
The paper presents the main characteristics and a preliminary implementation of a novel computational framework named Com-pLog. Inspired by probabilistic programming systems like ProbLog, Com-pLog builds upon the inferential mechanisms proposed by Simplicity Theory, relying on the computation of two Kolmogorov complexities (here implemented as min-path searches via ASP programs) rather than probabilistic inference. The proposed system enables users to compute ex-post and ex-ante measures of unexpectedness of a certain situation, mapping respectively to posterior and prior subjective probabilities. The computation is based on the specification of world and mental models by means of causal and descriptive relations between predicates weighted by complexity. The paper illustrates a few examples of application: generating relevant descriptions, and providing alternative approaches to disjunction and to negation.
Mots clés
Complexity-based programming Simplicity Theory Causal models Descriptive models Probability Relevant descriptions Negation Kolmogorov complexity Answer Set Programming
Complexity-based programming
Simplicity Theory
Causal models
Descriptive models
Probability
Relevant descriptions
Negation
Kolmogorov complexity
Answer Set Programming
Domaines
Informatique [cs]Origine | Fichiers produits par l'(les) auteur(s) |
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