  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.
