Abstract. We present a domain theoretic framework for obtaining exact solutions of linear boundary value problems. Based on the domain of compact real intervals, we show how to app...
Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabil...
We examine the problems with automated recommendation systems when information about user preferences is limited. We equate the problem to one of content similarity measurement an...
Abstract. Labelled Markov processes are continuous-state fully probabilistic labelled transition systems. They can be seen as co-algebras of a suitable monad on the category of mea...
Philippe Chaput, Vincent Danos, Prakash Panangaden...
Management of outer edge domains is a big challenge for service providers due to the diversity, heterogeneity and large amount of such networks, together with limited visibility on...
Pablo Arozarena, Raquel Toribio, Jesse Kielthy, Ke...