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Publicatie
Encoding Markov logic networks in possibilistic logic
Boekbijdrage - Boekhoofdstuk Conferentiebijdrage
Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue,
we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a given MLN using maximum a posteriori (MAP) inference. Unfortunately, the size of this theory is exponential in general. We therefore also propose two methods which can
derive compact theories that still capture MAP inference, but only for specific types of evidence. These theories can be used, among others, to make explicit the hidden assumptions underlying an MLN or to explain the predictions it makes.
Boek: Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence
Pagina's: 454 - 463
ISBN:978-0-9966431-0-8
Jaar van publicatie:2015
Toegankelijkheid:Open