Probabilistic Conceptual Network: A Belief Representation Scheme for Utility-Based Categorization

TL;DR

This paper introduces a probabilistic conceptual network scheme that integrates AI hierarchies and probabilistic networks to improve reasoning and decision-making in utility-based categorization tasks.

Contribution

It presents a novel knowledge representation framework combining abstraction hierarchies and probabilistic reasoning for dynamic categorization models.

Findings

Applied to automated machining for state reasoning

Enabled dynamic construction of categorization models

Improved decision support in uncertain environments

Abstract

Probabilistic conceptual network is a knowledge representation scheme designed for reasoning about concepts and categorical abstractions in utility-based categorization. The scheme combines the formalisms of abstraction and inheritance hierarchies from artificial intelligence, and probabilistic networks from decision analysis. It provides a common framework for representing conceptual knowledge, hierarchical knowledge, and uncertainty. It facilitates dynamic construction of categorization decision models at varying levels of abstraction. The scheme is applied to an automated machining problem for reasoning about the state of the machine at varying levels of abstraction in support of actions for maintaining competitiveness of the plant.