This short paper overviews 20 years of work done in logicbased Machine Learning and Data Mining along three di erent directions of research. The aim is to discuss the achievements and the open issues with reference to some challenging applications which involve representation and reasoning.
The current hype about AI is mainly due to a number of successful applications
of Machine Learning (ML) and Data Mining (DM) algorithms in challenging
domains such as vision. Most of these algorithms belong to the new generation
of neural networks known under the name of \deep learning". Deep learning
follows a function-based approach, i.e., it formulates ML tasks as function- tting
problems. Neural networks are therefore considered as examples of model-free AI
according to the de nition given by Hector Ge ner in his keynote talk at IJCAI
2018 [
As a drawback of the popularity of deep learning, the emerging trend is to
have ML streamlined into neural network research. Yet, the variety of ML and
DM algorithms is wide enough to have a signi cant overlap with model-based AI.
Inductive Logic Programming (ILP) [
In the following section I will overview the work done in ILP over the past 20 years along three di erent directions of research. The aim is to discuss the achievements and the open issues with reference to some challenging applications which involve representation and reasoning. In Section 3 I will conclude the paper with nal remarks. 2 2.1
Rules are widely used in Knowledge Engineering (KE) as a powerful way of
modeling knowledge. However, the acquisition of rules for very large Knowledge
Bases (KBs) still remains a very demanding KE activity. A partial automation of
the rule authoring task can be of help even though the automatically produced
rules are not guaranteed to be correct. A viable solution to this KE bottleneck is
just applying ILP algorithms. ILP has been historically concerned with learning
rules from examples and background knowledge with the aim of prediction (see,
e.g., the system Foil [
With the advent of the Semantic Web new challenges and opportunities have
been presented to ILP. In particular, ontologies and their logical foundations in
the family of Description Logics (DLs) [
Starting from the seminal work by Rouveirol and Ventos [
Imprecision is a weak form of vagueness, not to be mistaken for uncertainty,
which is often formalized with fuzzy set theory. For instance, spatial notions
such as the distance between two sites can be naturally represented with fuzzy
sets (modeling the degrees of distance, e.g., high, medium and low) if one is
interested in their human perception rather than in precise measurements. In
order to deal with imprecision in Ontology Reasoning several fuzzy extensions
of DLs have been proposed (see, e.g., [
The problem of automatically managing the evolution of fuzzy DL
ontologies has attracted some interest in the ILP community [
Imprecision dealt with fuzzy sets is strongly related to the notion of
information granule. In [
Research in ML and DM has traditionally focussed on designing e ective
algorithms for solving particular tasks, most of which can be seen as Constraint
Satisfaction Problems (CSPs) or Optimization Problems (OPs). However, there
is an increasing interest in providing the user with a means for specifying what
the ML/DM problem in hand actually is, rather than letting him struggle to
outline how the solution to that problem needs to be computed (see the recent note
by De Raedt [
for a comprehensive account). The model+solver approach is also at the basis of
Meta-Interpretive Learning (MIL) [
The importance of metamodeling in several applications has been recently
recognized in the KR community, with an increasing interest in higher-order
DLs. In particular, De Giacomo et al. [
Initiatives such as the workshop series promoted by the Association for NeuroSymbolic Integration (NeSy)3 since 2005 testify the need to address a fundamental open issue in AI: How to come up with a computational model capable of learning and reasoning both at the symbolic and the sub-symbolic level?
One such issue is also addressed by the Angry Birds AI4 competition, built around what is currently considered a challenging problem for AI: to build an intelligent agent that can play new levels of the Angry Birds game better than the best human players. This is a very di cult problem as it requires agents to predict the outcome of physical actions without having complete knowledge of the world, and then to select a good action out of in nitely many possible actions. A distinguishing feature of future AI systems is just this capability of interacting with the physical world. The Angry Birds AI competition provides a simpli ed and controlled environment for developing and testing this capability.
The ILP works overviewed in this short paper testify an e ort towards the integration between learning and reasoning, mostly at the symbolic level. However, the use of fuzzy logic could be considered as an attempt at dealing with the sub-symbolic level. Also, as opposed to neural networks, fuzzy systems have the potential of being interpretable and explainable.
A notorious drawback for ILP is the cost of computation. One of the advantages of the model+solver approach should be just to choose the most e cient solver to improve the performance of the learning process while preserving the
declarativity of the model. In this respect, Ge ner's vision [
Acknowledgments This work was partially funded by the INdAM - GNCS Project 2019 \Metodi per il trattamento di incertezza ed imprecisione nella rappresentazione e revisione di conoscenza", and by the Universita degli Studi di Bari \Aldo Moro" under the IDEA Giovani Ricercatori 2011 grant \Dealing with Vague Knowledge in Ontology Re nement".