Develops a new logic paradigm which emphasizes evidence tracking, including theory, connections to other fields, and sample applications.

This thesis studies the combination of two well known formal systems for knowledge representation: probabilistic logic and justification logic. Our aim is to design a formal framework that allows the analysis of epistemic situations with incomplete information. In order to achieve this we introduce two probabilistic justification logics, which are defined by adding probability operators to the minimal justification logic J. We prove soundness and completeness theorems for our logics and establish decidability procedures. Both our logics rely on an infinitary rule so that strong completeness can be achieved. One of the most interesting mathematical results for our logics is the fact that adding only one iteration of the probability operator to the justification logic J does not increase the computational complexity of the logic.

This book constitutes the proceedings of the 9th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2016, held in Linz, Austria, in March 2016. The 14 revised full papers presented papers were carefully reviewed and selected from 23 submissions. The papers address various topics such as reasoning about beliefs, uncertainty, incompleteness, and inconsistency, inference and problem solving, querying and pattern mining, dealing with knowledge, logics and complexity.