LFCS seminar: Vaishak Belle: Firstorder probabilistic relational models
What 


When 
Oct 31, 2017 from 04:00 PM to 05:00 PM 
Where  IF 4.31/4.33 
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Weighted model counting extends #SAT — the prototypical #Phard problem — in summing the weights of the models of a propositional formula. It has recently emerged as an effective and general approach to probabilistic inference, and has allowed practitioners to reason about heterogeneous statisticallogical representations, such as Markov logic networks, probabilistic databases, relational Bayesian networks and ProbLog programs, by encoding them as a logical theory. However, much of this work has been limited to an essentially propositional setting: the logical model is understood in terms of ground formulas over a fixed and finite domain; no infinite domains, and certainly no function symbols (other than constants). On the one hand, this is not surprising, because such features are very problematic from a decidability viewpoint, but on the other, they turn out to be very attractive from the point of view of machine learning applications when there is uncertainty about the existence and identity of objects. In this paper, we reconsider the problem of probabilistic reasoning in a logical language with infinite domains and function symbols, and establish some key results that permit effective algorithms.
The talk is based on papers that appeared at AAAI17 and UAI17.