From Probabilistic Programming to Complexity-based Programming
It offers a novel approach for AI systems dealing with uncertainty and reasoning, though it appears incremental as it builds on existing theories like Simplicity Theory.
The paper introduces CompLog, a computational framework that replaces probabilistic inference with Kolmogorov complexity-based measures to compute unexpectedness in situations, enabling applications like generating descriptions and handling disjunction and negation.
The paper presents the main characteristics and a preliminary implementation of a novel computational framework named CompLog. Inspired by probabilistic programming systems like ProbLog, CompLog 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.