In this chapter we present the recent results on constraint acquisition obtained by the Coconut team and their collaborators. In a first part we show how to 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 algorithm, called QUACQ, that, given a negative example, finds a constraint of the target network in a number of queries logarithmic in the size of the example. In a second part, we show that using some background knowledge may improve the acquisition process a lot. We introduce the concept of generalization query based on an aggregation of variables into types. We propose a generalization algorithm together with several strategies that we incorporate in QUACQ. Finally we evaluate our algorithms on some benchmarks.
During the last decade several constraint acquisition systems have been proposed for assisting non-expert users in building constraint programming models. GENACQ is an algorithm based on generalization queries that can be plugged into many constraint acquisition systems. However, generalization queries require the aggregation of variables into types which is not always a simple task for non-expert users. In this paper, we propose a new algorithm that is able to learn types during the constraint acquisition process. The idea is to infer potential types by analyzing the structure of the current constraint network and to use the extracted types to ask generalization queries. Our approach gives good results although no knowledge on the types is provided.
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