Argument Calculus and Networks

TL;DR

This paper introduces argument networks, a graphical framework inspired by Bayesian networks, to facilitate reasoning in propositional argument databases, enabling applications like nonmonotonic reasoning and diagnosis.

Contribution

It proposes a novel graphical representation called argument networks and an associated reasoning algorithm for propositional argument databases.

Findings

Argument networks resemble Bayesian networks in structure.

The reasoning algorithm is efficient and similar to Bayesian network algorithms.

Applications include nonmonotonic reasoning, truth maintenance, and diagnosis.

Abstract

A major reason behind the success of probability calculus is that it possesses a number of valuable tools, which are based on the notion of probabilistic independence. In this paper, I identify a notion of logical independence that makes some of these tools available to a class of propositional databases, called argument databases. Specifically, I suggest a graphical representation of argument databases, called argument networks, which resemble Bayesian networks. I also suggest an algorithm for reasoning with argument networks, which resembles a basic algorithm for reasoning with Bayesian networks. Finally, I show that argument networks have several applications: Nonmonotonic reasoning, truth maintenance, and diagnosis.