Automatically constructing novel representations of tasks from analysis of state spaces is a longstanding fundamental challenge in AI. I review recent progress on this problem for...
We propose a method of approximate dynamic programming for Markov decision processes (MDPs) using algebraic decision diagrams (ADDs). We produce near-optimal value functions and p...
Abstract. Reference process models capture common practices in a given domain and variations thereof. Such models are intended to be configured in a specific setting, leading to in...
Marcello La Rosa, Florian Gottschalk, Marlon Dumas...
We propose an algorithm for the binarization of document images degraded by uneven light distribution, based on the Markov Random Field modeling with Maximum A Posteriori probabil...
Abstract— This paper introduces a novel architecture for performing the core computations required by dynamic programming (DP) techniques. The latter pertain to a vast range of a...