The large and still increasing popularity of deep learning clashes with a major limit of neural
network architectures, that consists in their lack of capability in providing human …

This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …

While constraints are ubiquitous in artificial intelligence and constraints are also commonly
used in machine learning and data mining, the problem of learning constraints from …

In the last few years we have seen a remarkable progress from the cultivation of the idea of
expressing domain knowledge by the mathematical notion of constraint. However, the …

A significant challenge in declarative approaches to scheduling is the creation of a model:
the set of resources and their capacities and the types of activities and their temporal and …

Constraint acquisition can assist non-expert users to model their problems as constraint
networks. In active constraint acquisition, this is achieved through an interaction between the …

Conceptual modeling plays a fundamental role to capture information about complex
business domains (eg, finance, healthcare) and enables semantic interoperability. To fulfill …

Conceptual modeling plays a fundamental role in information systems engineering, and in
data and systems interoperability. To play their role as instruments for domain modeling …

Solving Constraint Optimization Problems (COPs) can be dramatically simplified by
boundary estimation, that is providing tight boundaries of cost functions. By feeding a …

This paper presents the industrial manufacturing problem, by using the ILP (Integer Linear
Programming) method, which will help us to get closer to the SAT (SATisfiability) of …