Maximum Entropy Applied to Inductive Logic and Reasoning

Abstract

This editorial explains the scope of the special issue and provides a thematic introduction to the contributed papers.Keywords: entropy; MaxEnt; inductive logic; reasoning With this special issue, we wanted to provide a platform for practitioners, proponents and opponents of Maximum Entropy methods (MaxEnt) applied to inductive logic and reasoning. Unfortunately, we received no papers arguing against MaxEnt. However, we did receive an exciting array of positive contributions, which are described below.There has been much debate in the inductive logic and reasoning literature on the justificatory status of MaxEnt. Jeff Paris, in his paper [1], defends MaxEnt against the charge of language dependence and warns against mistaking subjective degrees of belief for estimates of objective probabilities and mis-applying the Principle of Insufficient Reason.The other contributed papers focus on extensions of the standard framework for MaxEnt in inductive logic and reasoning. The standard framework considers a single agent equipped with a finite propositional language (or a finite domain of propositions) and a propositional knowledge base, seeking to determine probabilities from these ingredients. Papers in this volume extend the standard framework in four different directions: the multi-agent setting, the dynamic setting, more elaborate knowledge bases, and richer underlying languages.