There exists a gap between visualization design guidelines and their application in
visualization tools. While empirical studies can provide design guidance, we lack a formal …

The performance of many machine learning methods depends critically on hyperparameter
settings. Sophisticated Bayesian optimization methods have recently achieved considerable …

Abstract Answer Set Programming (ASP) is a model, ground, and solve paradigm. The
integration of application-or theory-specific reasoning into ASP systems thus impacts on …

Answer Set Programming (ASP) is a declarative problem solving approach, initially tailored
to modeling problems in the area of Knowledge Representation and Reasoning (KRR) …

Our world and our lives are changing in many ways. Communication, networking, and
computing technologies are among the most influential enablers that shape our lives today …

Deep reinforcement learning (DRL) has gained great success by learning directly from high-
dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of …

Potassco: The Potsdam Answer Set Solving Collection Page 1 AI Communications 24 (2011)
107–124 107 DOI 10.3233/AIC-2011-0491 IOS Press Potassco: The Potsdam Answer Set …

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce
a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we …

Reinforcement learning and symbolic planning have both been used to build intelligent
autonomous agents. Reinforcement learning relies on learning from interactions with real …

Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of
interpretability, and a need for large amounts of training data. We survey recent work in …