ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Act, and the rest will follow: exploiting determinism in planning as satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A linear-time transformation of linear inequalities into conjunctive normal form
Information Processing Letters
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Unrestricted vs restricted cut in a tableau method for Boolean circuits
Annals of Mathematics and Artificial Intelligence
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
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
E-RES: Reasoning about Actions, Events and Observations
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
SAT-Based Planning with Minimal-#actions Plans and "soft" Goals
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Computing All Optimal Solutions in Satisfiability Problems with Preferences
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Anytime heuristic search for partial satisfaction planning
Artificial Intelligence
A heuristic search approach to planning with temporally extended preferences
Artificial Intelligence
A new Approach for Solving Satisfiability Problems with Qualitative Preferences
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
On solving Boolean multilevel optimization problems
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Improving Plan Quality in SAT-Based Planning
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Solving satisfiability problems with preferences
Constraints
A weighted CSP approach to cost-optimal planning
AI Communications
Specifying and computing preferred plans
Artificial Intelligence
µ-SATPLAN: Multi-agent planning as satisfiability
Knowledge-Based Systems
Boolean lexicographic optimization: algorithms & applications
Annals of Mathematics and Artificial Intelligence
SAS+ planning as satisfiability
Journal of Artificial Intelligence Research
Planning as satisfiability with IPC simple preferences and action costs
AI Communications
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Planning as Satisfiability is one of the most well-known and effective technique for classical planning: SATPLAN has been the winning system in the deterministic track for optimal planners in the 4th International Planning Competition (IPC) and a co-winner in the 5th IPC. In this paper we extend the Planning as Satisfiability approach in order to handle preferences and SATPLAN in order to solve problems with simple preferences. The resulting system, SATPLAN(P) is competitive with SGPLAN, the winning system in the category "simple preferences" at the last IPC. Further, we show that SATPLAN(P) performances are (almost) always comparable to those of SATPLAN when solving the same problems without preferences: in other words, introducing simple preferences in SATPLAN does not affect its performances. This latter result is due both to the particular mechanism we use in order to incorporate preferences in SAT-PLAN and to the relative low number of soft goals (each corresponding to a simple preference) usually present in planning problems. Indeed, if we consider the issue of determining minimal plans (corresponding to problems with thousands of preferences) the performances of SATPLAN(P) are comparable to those of SATPLAN in many cases, but can be significantly worse when the number of preferences is very high compared to the total number of variables in the problem. Our analysis is conducted considering both qualitative and quantitative preferences, different reductions from quantitative to qualitative ones, and most of the propositional planning domains from the IPCs and that SATPLAN can handle.