GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
The Complexity of Resolution Refinements
LICS '03 Proceedings of the 18th Annual IEEE Symposium on Logic in Computer Science
Comparing Arguments Using Preference Orderings for Argument-Based Reasoning
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Unrestricted vs restricted cut in a tableau method for Boolean circuits
Annals of Mathematics and Artificial Intelligence
A dynamic topological sort algorithm for directed acyclic graphs
Journal of Experimental Algorithmics (JEA)
Solving Optimization Problems with DLL
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
A new Approach for Solving Satisfiability Problems with Qualitative Preferences
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Planning as satisfiability with preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Solving satisfiability problems with preferences
Constraints
Proceedings of the 2011 ACM Symposium on Applied Computing
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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The ability to effectively reason in the presence of qualitative preferences on literals or formulas is a central issue in Artificial Intelligence. In the last few years, two procedures have been presented in order to reason with propositional satisfiability SAT problems in the presence of additional, partially ordered qualitative preferences on literals or formulas: the first requires a modification of the branching heuristic of the SAT solver in order to guarantee that the first solution is optimal, while the second computes a sequence of solutions, each guaranteed to be better than the previous one. The two approaches have their own advantages and disadvantages and when compared on specific classes of instances --each having an empty partial order --the second seems to have superior performance.In this paper we show that the above two approaches for reasoning with qualitative preferences can be combined yielding a new effective procedure. In particular, in the new procedure we modify the branching heuristic --as in the first approach --by possibly changing the polarity of the returned literal, and then we continue the search --as in the second approach --looking for better solutions. We extended the experimental analysis conducted in previous papers by considering a wide variety of problems, having both an empty and a non-empty partial order: the results show that the new procedure performs better than the two previous approaches on average, and especially on the “hard” problems. As a preliminary result, we show that the framework of qualitative preferences on literals is more general and expressive than the framework on quantitative preferences.