Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed provide insights into the GNN decision making process. These are first important steps on way explainability, but generated explanations often hard understand for users that not AI experts. To overcome this problem, we introduc...

This paper presents a feasibility study for implementing lexical morphology principles in a machine translation system in order to solve unknown words. Multilingual symbolic treatment of word-formation is seducing but requires an in-depth analysis of every step that has to be performed. The construction of a prototype is firstly presented, highlighting the methodological issues of such approach...

We consider the problem of computing optimal generalised policies for relational Markov decision processes. We describe an approach combining some of the benefits of purely inductive techniques with those of symbolic dynamic programming methods. The latter reason about the optimal value function using first-order decisiontheoretic regression and formula rewriting, while the former, when provide...

We consider the problem of computing optimal generalised policies for relational Markov decision processes. We describe an approach combining some of the benefits of purely inductive techniques with those of symbolic dynamic programming methods. The latter reason about the optimal value function using first-order decisiontheoretic regression and formula rewriting, while the former, when provide...

Despite its many unquestionable benefits, formal specifications are not widely used in industrial software development. In order to reduce the time and effort required to write formal specifications, in this paper we propose a technique for automatically discovering specifications from real code. The proposed methodology relies on the symbolic execution capabilities recently provided by the K f...

An important part of diagram understanding is the problem of glyph recognition. This problem is challenging because a glyph may be drawn in many different ways and with varying levels of precision. Diagrams in general are rarely perfect and often filled with poorly-drawn glyphs. In order to understand a wide range of diagrams, a diagrammatic reasoner should recognize glyphs that deviate from st...