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A major reason behind the success of probability calculus is that itpossesses a number of valuable tools, which are based on the notion ofprobabilistic independence. In this paper, I identify a notion of logicalindependence that makes some of these tools available to a class ofpropositional databases, called argument databases. Specifically, I suggest agraphical representation of argument databases, called argument networks, whichresemble Bayesian networks. I also suggest an algorithm for reasoning withargument networks, which resembles a basic algorithm for reasoning withBayesian networks. Finally, I show that argument networks have severalapplications: Nonmonotonic reasoning, truth maintenance, and diagnosis.

Argument Calculus and Networks