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.

翻译：本文介绍了一种名为CompLog的新型计算框架的主要特征及初步实现。受ProbLog等概率编程系统的启发，CompLog基于简单性理论提出的推理机制，通过计算两种柯尔莫哥洛夫复杂度（此处通过ASP程序的最小路径搜索实现）替代概率推理。该框架使用户能够计算特定情境的事后与事前意外程度度量，分别对应后验主观概率与先验主观概率。计算过程基于世界模型与心智模型的规范，这些模型由谓词间的因果与描述性关系构成，并以复杂度作为权重。本文通过生成相关描述、提供析取与否定的替代方法等应用实例，展示了该框架的实践效果。