Rafael Nunez, Matthias Scheutz, Kamal Premaratne, Manohar N. Murthi

---

Modeling Uncertainty in First-Order Logic

Abstract

First order logic lies at the core of many methods in mathematics, philosophy, linguistics, and computer science. Although important efforts have been made to extend first order logic to the task of handling uncertainty, there is still a lack of a consistent and unified approach, especially within the Dempster-Shafer (DS) theory framework. In this work we introduce a systematic approach for building belief assignments based on first order logic formulas. Furthermore, we outline the foundations of Uncertain Logic, a robust framework for inference and modeling when information is available in the form of first order logic formulas subject to uncertainty. Applications include data fusion, rule mining, credibility estimation, crowd sourcing, among many others.

Keywords

Uncertain Logic, Uncertain Reasoning, Probabilistic Logic, Dempster-Shafer Theory, Belief Theory.

---

Download area

The paper is available in the following formats:

• pdf file

E-mail addresses

Rafael Nunez		nunez@umiami.edu
Matthias Scheutz		mscheutz@cs.tufts.edu
Kamal Premaratne		kamal@miami.edu
Manohar N. Murthi		mmurthi@miami.edu