Abstract The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the
performance of general artificial intelligence algorithms. The ARC's focus on broad …

Constraint programming is used to model and solve complex combinatorial problems. The
modeling task requires some expertise in constraint programming. This requirement is a …

We describe a system which generates finite domain constraint models from positive
example solutions, for highly structured problems. The system is based on the global …

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 …

We learn constraint networks by asking the user partial queries. That is, we ask the user to
classify assignments to subsets of the variables as positive or negative. We provide an …

Many planning, scheduling or multi-dimensional packing problems involve the design of
subtle logical combinations of temporal or spatial constraints. On the one hand, the precise …

We introduce the problem of learning SMT (LRA) constraints from data. SMT (LRA) extends
propositional logic with (in) equalities between numerical variables. Many relevant formal …

In a sequential auction with multiple bidding agents, the problem of determining the ordering
of the items to sell in order to maximize the expected revenue is highly challenging. The …

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 …