In a behavioral synthesis system, a typical approach used to guide the scheduler is to impose hard constraints on the relative timing between operations considering performance, a...
Soft constraints are a generalization of classical constraints, where constraints and/or partial assignments are associated to preference or importance levels, and constraints are...
Soft constraints are a generalization of classical constraints, which allow for the description of preferences rather than strict requirements. In soft constraints, constraints and...
Soft constraints extend classical constraints to deal with non-functional requirements, overconstrained problems and preferences. Bistarelli, Montanari and Rossi have developed a ...
Martin Wirsing, Grit Denker, Carolyn L. Talcott, A...
The constraint satisfaction problem (CSP) is a central generic problem in artificial intelligence. Considerable effort has been made in identifying properties which ensure tractabi...
Over-constrained problems can be solved with the help of soft constraints. Weighted constraints are a typical representation of soft constraints used to minimize weights of unsati...
Recent research in AI Planning is focused on improving the quality of the generated plans. PDDL3 incorporates hard and soft constraints on goals and the plan trajectory. Plan traj...
The semiring-based formalism to model soft constraint has been introduced in 1995 by Ugo Montanari and the authors of this paper. The idea was to make constraint programming more f...
The notion of optimality naturally arises in many areas of applied mathematics and computer science concerned with decision making. Here we consider this notion in the context of ...
Krzysztof R. Apt, Francesca Rossi, Kristen Brent V...