The generation of comprehensible explanations is an essential feature of
modern artificial intelligence systems. In this work, we consider probabilistic
logic programming, an extension of logic programming which can be useful to
model domains with relational structure and uncertainty. Essentially, a program
specifies a probability distribution over possible worlds (i.e., sets of
facts). The notion of explanation is typically associated with that of a world,
so that one often looks for the most probable world as well as for the worlds
where the query is true. Unfortunately, such explanations exhibit no causal
structure. In particular, the chain of inferences required for a specific
prediction (represented by a query) is not shown. In this paper, we propose a
novel approach where explanations are represented as programs that are
generated from a given query by a number of unfolding-like transformations.
Here, the chain of inferences that proves a given query is made explicit.
Furthermore, the generated explanations are minimal (i.e., contain no
irrelevant information) and can be parameterized w.r.t. a specification of
visible predicates, so that the user may hide uninteresting details from
explanations.Comment: Published as: Vidal, G. (2022). Explanations as Programs in
Probabilistic Logic Programming. In: Hanus, M., Igarashi, A. (eds) Functional
and Logic Programming. FLOPS 2022. Lecture Notes in Computer Science, vol
13215. Springer, Cham. The final authenticated publication is available
online at https://doi.org/10.1007/978-3-030-99461-7_1