From Probabilistic Programming to Complexity-based Programming

Giovanni Sileno (1) , Jean-Louis Dessalles (2, 3)

1 UvA - University of Amsterdam [Amsterdam] = Universiteit van Amsterdam
2 DIG - Data, Intelligence and Graphs
3 INFRES - Département Informatique et Réseaux

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.

Keywords: 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

Domains: Computer Science [cs]

Giovanni Sileno, Jean-Louis Dessalles. From Probabilistic Programming to Complexity-based Programming. 26th European Conference on Artificial Intelligence ECAI 2023, Sep 2023, Kraków, Poland. pp.304-317, ⟨10.1007/978-3-031-50485-3_32⟩. ⟨hal-04464156⟩