Solving quantified constraint satisfaction problems

We make a number of contributions to the study of the Quantified Constraint Satisfaction Problem (QCSP). The QCSP is an extension of the constraint satisfaction problem that can be used to model combinatorial problems containing contingency or uncertainty. It allows for universally quantified variables that can model uncertain actions and events, such as the unknown weather for a future party, or an opponent's next move in a game. In this paper we report significant contributions to two very different methods for solving QCSPs. The first approach is to implement special purpose algorithms for QCSPs; and the second is to encode QCSPs as Quantified Boolean Formulas and then use specialized QBF solvers. The discovery of particularly effective encodings influenced the design of more effective algorithms: by analyzing the properties of these encodings, we identify the features in QBF solvers responsible for their efficiency. This enables us to devise analogues of these features in QCS...

• Type: Journal
• Year: 2008
• Where: AI
• Authors: Ian P. Gent, Peter Nightingale, Andrew G. D. Rowley, Kostas Stergiou
• Keywords: AI 2008, Artificial Intelligence, Constraint Satisfaction Problem, Quantified Constraint Satisfaction, Special Purpose Algorithms