Confidences for Commonsense Reasoning

Commonsense reasoning has long been considered one of the holy grails of artificial intelligence. Our goal is to develop a logic-based component for hybrid - machine learning plus logic - commonsense question answering systems. A critical feature for the component is estimating the confidence in the statements derived from knowledge bases containing uncertain contrary and supporting evidence obtained from different sources. Instead of computing exact probabilities or designing a new calculus we focus on extending the methods and algorithms used by the existing automated reasoners for full classical first-order logic. The paper presents the CONFER framework and implementation for confidence estimation of derived answers.