A General Purpose Inference Engine for Evidential Reasoning Research

TL;DR

This paper introduces a versatile inference engine designed for evidential reasoning, enabling improved representation and manipulation of uncertainty in automated systems, building on prior research with scalar uncertainty calculi.

Contribution

The paper presents a new general-purpose inference engine that supports a broader range of uncertainty representations and calculi for automated reasoning systems.

Findings

Enhanced reasoning capabilities with diverse uncertainty calculi

Successful implementation of the inference engine in experimental setups

Insights into the effects of different uncertainty representations

Abstract

The purpose of this paper is to report on the most recent developments in our ongoing investigation of the representation and manipulation of uncertainty in automated reasoning systems. In our earlier studies (Tong and Shapiro, 1985) we described a series of experiments with RUBRIC (Tong et al., 1985), a system for full-text document retrieval, that generated some interesting insights into the effects of choosing among a class of scalar valued uncertainty calculi. [n order to extend these results we have begun a new series of experiments with a larger class of representations and calculi, and to help perform these experiments we have developed a general purpose inference engine.