Constraint Acquisition (CA) systems can be used to assist in the modeling of constraint
satisfaction problems. In (inter) active CA, the system is given a set of candidate constraints …

Learning constraint networks is known to require a number of membership queries
exponential in the number of variables. In this paper, we learn constraint networks by asking …

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 …

Constraint Programming (CP) has been successfully used to model and solve complex
combinatorial problems. However, modeling is often not trivial and requires expertise, which …

A constraint-based model represents knowledge about a domain by a set of constraints,
which must be satisfied by solutions in that domain. These models may be used for …

Modeling a combinatorial problem is a hard and error-prone task requiring significant
expertise. Constraint acquisition methods attempt to automate this process by learning …

MQuAcq is an algorithm for active constraint acquisition that has been shown to outperform
previous algorithms such as QuAcq and MultiAcq. In this paper, we exhibit two important …

Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to
model their problems as constraint networks by classifying (partial) examples as positive or …

Interactive constraint acquisition is a special case of query-directed learning, also known as
“exact” learning. It is used to assist non-expert users in modeling a constraint problem …

Modeling a combinatorial problem is a hard and error-prone task requiring expertise.
Constraint acquisition methods can automate this process by learning constraints from …