Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning
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In this paper, we extend Dung's seminal argument framework to form a probabilistic argument framework by associating probabilities with arguments and defeats. We then compute the likelihood of some set of arguments appearing within an arbitrary argument framework induced from this probabilistic framework. We show that the complexity of computing this likelihood precisely is exponential in the number of arguments and defeats, and thus describe an approximate approach to computing these likelihoods based on Monte-Carlo simulation. Evaluating the latter approach against the exact approach shows significant computational savings. Our probabilistic argument framework is applicable to a number of real world problems; we show its utility by applying it to the problem of coalition formation.