Knowledge Engineering Within A Generalized Bayesian Framework

TL;DR

This paper introduces a Generalized Bayesian framework for expert systems that effectively handle uncertainty, offering new tools for knowledge engineering, inference, and explanation in AI systems.

Contribution

It presents a novel Bayesian-based architecture for expert systems that is neither rule-based nor frame-based, with specialized knowledge engineering tools.

Findings

Provides a flexible inference engine for uncertain domains

Includes explanation capabilities for transparency

Offers tools for building consistent knowledge bases

Abstract

During the ongoing debate over the representation of uncertainty in Artificial Intelligence, Cheeseman, Lemmer, Pearl, and others have argued that probability theory, and in particular the Bayesian theory, should be used as the basis for the inference mechanisms of Expert Systems dealing with uncertainty. In order to pursue the issue in a practical setting, sophisticated tools for knowledge engineering are needed that allow flexible and understandable interaction with the underlying knowledge representation schemes. This paper describes a Generalized Bayesian framework for building expert systems which function in uncertain domains, using algorithms proposed by Lemmer. It is neither rule-based nor frame-based, and requires a new system of knowledge engineering tools. The framework we describe provides a knowledge-based system architecture with an inference engine, explanation capability, and a unique aid for building consistent knowledge bases.