Abductive inferencing for integrating information from human and robotic sources

Abstract

Abductive inference (best-explanation reasoning) is a useful conceptual framework for analyzing and implementing the inferencing needed to integrate information from human and robotic sources. Inferencing proceeds from reports, to explanations for these reports, given in terms of hypothesized real-world entities and the processes by which the entities lead to the reports. Reports from humans and robotic sources are subject to different kinds of corruption, so they require different treatment as sources of evidence. The best explanation for a certain report might be that it presents a reliable statement that results from a chain of causality from the events reported, to their effects on human or robotic senses, and from there through transduction, processing, and reporting. Confidence in this explanation will be undercut by evidence supporting a rival explanation, such as one involving error or intended deception.