Defaults and Infinitesimals: Defeasible Inference by Nonarchimedean Entropy-Maximization

This work addresses reasoning under uncertainty for AI and logic communities, presenting a novel theoretical approach rather than an incremental improvement.

The paper tackles the problem of defeasible inference by introducing a new semantics based on infinitesimal probabilities and entropy maximization, resulting in a framework that interprets defaults as generalized conditional probability constraints.