Possibilistic constraint satisfaction problems or “how to handle soft constraints?”
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
Artificial Intelligence
Making social choices from individuals' CP-nets
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Managing complex scheduling problems with dynamic and hybrid constraints
Managing complex scheduling problems with dynamic and hybrid constraints
Journal of Artificial Intelligence Research
Learning conditional preference networks with queries
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Computational agents can assist people by guiding their decisions in ways that achieve their goals while also adhering to constraints on their actions. Because some domains are more naturally modeled by representing preferences and constraints separately, we seek to develop efficient techniques for solving such decoupled constrained optimization problems. This paper describes a parameterized formulation for decoupled constrained optimization problems that subsumes the state-of-the-art algorithm of Boutilier et al., representing a wider family of alternative algorithms. We empirically examine notable members of this family to highlight the spaces of decoupled constrained optimization problems for which each excels, highlight fundamental relationships between different algorithmic variations, and use these insights to create and evaluate novel hybrids of these algorithms that a cognitive assistant agent can use to flexibly trade off solution quality with computational time.