Argument Calculus and Networks
This work addresses a foundational gap in logical reasoning for AI and computer science, though it appears incremental as it adapts existing probabilistic tools to a new context.
The paper tackles the lack of logical independence tools in propositional databases by introducing argument networks, a graphical representation similar to Bayesian networks, and shows applications in nonmonotonic reasoning, truth maintenance, and diagnosis.
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.