  The ever increasing complexity of machine learning techniques used more and
more in practice, gives rise to the need to explain the predictions and
decisions of these models, often used as black-boxes. Explainable AI approaches
are either numerical feature-based aiming to quantify the contribution of each
feature in a prediction or symbolic providing certain forms of symbolic
explanations such as counterfactuals. This paper proposes a generic agnostic
approach named ASTERYX allowing to generate both symbolic explanations and
score-based ones. Our approach is declarative and it is based on the encoding
of the model to be explained in an equivalent symbolic representation, this
latter serves to generate in particular two types of symbolic explanations
which are sufficient reasons and counterfactuals. We then associate scores
reflecting the relevance of the explanations and the features w.r.t to some
properties. Our experimental results show the feasibility of the proposed
approach and its effectiveness in providing symbolic and score-based
explanations.
