Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
A symbolic generalization of probability theory
A symbolic generalization of probability theory
Found ations of assumption-based truth maintenance systems: preliminary report
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
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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.