The goal of this note is to provide a background and references for the invited lecture presented at Computer Science Logic 2006. We briefly discuss motivations that led to the eme...
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. denite clause programs containing probabilistic facts with a ...
The relationship between abstract interpretation and partial deduction has received considerable attention and (partial) integrations have been proposed starting from both the part...
This paper focuses on the problem of scheduling outages to computer systems in complex distributed environments. The interconnected nature of these systems makes scheduling global ...
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...