Enhancing Constraint Acquisition Through Hybrid Learning: An Integration of Passive and Active Learning Strategies.

Constraint Programming (CP) is a successful methodology for solving combinatorial problems from various domains. Efficiently modeling the problem at hand as a Constraint Satisfaction Problem is a crucial, but difficult task in CP. Toward this, a recent approach that is attracting increasing interest is Constraint Acquisition, i.e., the (semi)automatic learning of constraints through examples of solutions and non-solutions. This paper introduces a hybrid methodology that combines passive and active learning strategies to acquire both global and fixed arity constraints. This hybrid approach leverages the strengths of both techniques to address their individual limitations. Passive learning rapidly learns constraints from example solutions, while active learning refines and contextualizes constraints through user interaction. The core of the methodology consists of a passive learning module where subsets of variables are compared against global constraints and are validated using a CP solver. Constraints consistently present across multiple solution sets are identified as global constraints that belong to the model. Then, fixed arity constraints are refined through an active learning module with user input. Experiments across various problem types, from simple to complex, demonstrate the efficiency of the proposed hybrid methodology. [ABSTRACT FROM AUTHOR]