Daria Stepanova

Angry-HEX: An Artificial Player for Angry Birds Based on Declarative Knowledge Bases

This paper presents the Angry-HEX artificial intelligent agent that participated in the 2013 and 2014 Angry Birds Artificial Intelligence Competitions. The agent has been developed in the context of a joint project between the University of Calabria (UniCal) and the Vienna University of Technology (TU Vienna). The specific issues that arise when introducing artificial intelligence in a physics-based game are dealt with a combination of traditional imperative programming and declarative programming, used for modeling discrete knowledge about the game and the current situation. In particular, we make use of HEX programs, which are an extension of answer set programming (ASP) programs toward integration of external computation sources, such as 2-D physics simulation tools.

Exception-Enriched Rule Learning from Knowledge Graphs

Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. These KGs are inevitably bound to be incomplete. To fill in the gaps, data correlations in the KG can be analyzed to infer Horn rules and to predict new facts. However, Horn rules do not take into account possible exceptions, so that predicting facts via such rules introduces errors. To overcome this problem, we present a method for effective revision of learned Horn rules by adding exceptions (i.e., negated atoms) into their bodies. This way errors are largely reduced. We apply our method to discover rules with exceptions from real-world KGs. Our experimental results demonstrate the effectiveness of the developed method and the improvements in accuracy for KG completion by rule-based fact prediction.

Completeness-aware Rule Learning from Knowledge Graphs

Knowledge graphs (KGs) are huge collections of primarily encyclopedic facts. They are widely used in entity recognition, structured search, question answering, and other important tasks. Rule mining is commonly applied to discover patterns in KGs. However, unlike in traditional association rule mining, KGs provide a setting with a high degree of incompleteness, which may result in the wrong estimation of the quality of mined rules, leading to erroneous beliefs such as all artists have won an award, or hockey players do not have children.

Towards practical deletion repair of inconsistent DL-programs

Nonmonotonic Description Logic (DL-) programs couple nonmonotonic logic programs with DL-ontologies through queries in a loose way which may lead to inconsistency, i.e., lack of an answer set. Recently defined repair answer sets remedy this but a straightforward computation method lacks practicality. We present a novel evaluation algorithm for deletion repair answer sets based on support sets, which reduces evaluation of DL-LiteA ontology queries to constraint matching. This leads to significant performance gains towards inconsistency management in practice.

Towards Nonmonotonic Relational Learning from Knowledge Graphs

Recent advances in information extraction have led to the so-called knowledge graphs (KGs), i.e., huge collections of relational factual knowledge. Since KGs are automatically constructed, they are inherently incomplete, thus naturally treated under the Open World Assumption (OWA). Rule mining techniques have been exploited to support the crucial task of KG completion. However, these techniques can mine Horn rules, which are insufficiently expressive to capture exceptions, and might thus make incorrect predictions on missing links. Recently, a rule-based method for filling in this gap was proposed which, however, applies to a flattened representation of a KG with only unary facts. In this work we make the first steps towards extending this approach to KGs in their original relational form, and provide preliminary evaluation results on real-world KGs, which demonstrate the effectiveness of our method.

Computing Repairs for Inconsistent DL-programs over

DL-programs couple nonmonotonic logic programs with DL- ontologies through queries in a loose way which may lead to inconsistency, i.e., lack of an answer set. Recently defined repair answer sets remedy this. In particular, for

\mathcal{EL}

DL-Lite_{\mathcal{A}}

Ontologies

ontologies, the computation of deletion repair answer sets can effectively be reduced to constraint matching based on so-called support sets. Here we consider the problem for DL-programs over

Inconsistency management for description logic programs and beyond

\mathcal{EL}

Semantic independence in DL-programs

ontologies. This is more challenging than adopting a suitable notion of support sets and their computation. Compared to