Several queries and scores have recently been proposed to explain individual predictions
over ML models. Examples include queries based on “anchors”, which are parts of an …

Explanation is necessary for humans to understand and accept decisions made by an AI
system when the system's goal is known. It is even more important when the AI system …

Public health emergencies have brought great challenges to the stability of the e-commerce
supply chain. Demand forecasting is a key driver for the sound development of e-commerce …

We discuss some recent advances on combining data and knowledge in the context of
supervised learning using Bayesian networks. A first set of advances concern the …

Earlier studies have shown that classification accuracies of Bayesian networks (BNs)
obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the …

Abstract Machine learning and argumentation can potentially greatly benefit from each
other. Combining deep classifiers with knowledge expressed in the form of rules and …

Tractable Boolean and arithmetic circuits have been studied extensively in AI for over two
decades now. These circuits were initially proposed as “compiled objects,” meant to facilitate …

Realistically modelling behaviour and interaction of mixed road users (pedestrians and
vehicles) in shared spaces are challenging due to the heterogeneity of transport modes and …

We present new results on the classical algorithm of variable elimination, which underlies
many algorithms including for probabilistic inference. The results relate to exploiting …

Automation and an increasing level of autonomy are not novelties in aircraft cockpits. One of
the main goals of automation in aviation is to increase pilots' situation awareness (SA) and …