Introducing Preferences in Planning as Satisfiability

Planning as Satisfiability is one of the most well-known and effective techniques 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 cowinner in the 5th IPC. Given a planning problem Π and a makespan n, the approach based on satisfiability (a.k.a. SAT-based) simply works by (i) constructing a SAT formula Π n and (ii) checking Ðn for satisfiability: if there is a model for Π n then we have found a plan, otherwise n is increased. The approach guarantees that the makespan is optimal, i.e. minimum. In this article we extend the Planning as Satisfiability approach in order to handle preferences and satplan in order to solve problems with simple preferences. This allows, e.g. to take into consideration ‘plan quality’ issues other than makespan, like number of actions and ‘soft’ goals. The basic idea is to explore the search space of possible plans in accordance with the given partially ordered preferences.We first prove that, at fixed makespan, our approach returns an ‘optimal’ plan, if any. Then, considering both classical planning problems and problems coming from IPC-5, we show that satplan extended in order to deal with preferences: (i) returns optimal plans that are often of considerable better quality, i.e. with fewer actions or with a better plan metric on soft goals, than satplan; and (ii) is overall competitive, in terms of plan quality, with sgplan, the winning system in the ‘SimplePreferences’ category of the IPC-5. Notably, such results are often obtained without sacrificing efficiency.

[1]  Niklas Sörensson,et al.  An Extensible SAT-solver , 2003, SAT.

[2]  J. Ho,et al.  The Metric FF Planning System Translating Ignoring Delete Lists to Numeric State Variables , 2003 .

[3]  Derek Long,et al.  Plan Constraints and Preferences in PDDL3 , 2006 .

[4]  Jussi Rintanen,et al.  Planning for Temporally Extended Goals as Propositional Satisfiability , 2007, IJCAI.

[5]  Hector Geffner,et al.  Heuristics for Planning with Action Costs Revisited , 2008, ECAI.

[6]  S. Edelkamp,et al.  The Deterministic Part of IPC-4: An Overview , 2005, J. Artif. Intell. Res..

[7]  Craig Boutilier,et al.  CP-nets: a tool for represent-ing and reasoning with conditional ceteris paribus state-ments , 2004 .

[8]  Subbarao Kambhampati,et al.  Anytime heuristic search for partial satisfaction planning , 2009, Artif. Intell..

[9]  Craig A. Knoblock,et al.  Combining the Expressivity of UCPOP with the Efficiency of Graphplan , 1997, ECP.

[10]  Enrico Giunchiglia,et al.  SAT-Based Planning with Minimal-#actions Plans and "soft" Goals , 2007, AI*IA.

[11]  Enrico Giunchiglia,et al.  Act, and the Rest Will Follow: Exploiting Determinism in Planning as Satisfiability , 1998, AAAI/IAAI.

[12]  Vasco M. Manquinho,et al.  The First Evaluation of Pseudo-Boolean Solvers (PB'05) , 2006, J. Satisf. Boolean Model. Comput..

[13]  Enrico Giunchiglia,et al.  Solving Optimization Problems with DLL , 2006, ECAI.

[14]  Sheila A. McIlraith,et al.  Planning with Qualitative Temporal Preferences , 2006, KR.

[15]  Subbarao Kambhampati,et al.  Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective , 2011, J. Artif. Intell. Res..

[16]  Jorge A. Baier,et al.  A Heuristic Search Approach to Planning with Temporally Extended Preferences , 2007, IJCAI.

[17]  David A. Plaisted,et al.  A Structure-Preserving Clause Form Translation , 1986, J. Symb. Comput..

[18]  Thomas Eiter,et al.  Preferred Answer Sets for Extended Logic Programs , 1999, Artif. Intell..

[19]  Yixin Chen,et al.  Long-Distance Mutual Exclusion for Propositional Planning , 2007, IJCAI.

[20]  Dana S. Nau,et al.  SHOP2: An HTN Planning System , 2003, J. Artif. Intell. Res..

[21]  Edwin P. D. Pednault,et al.  ADL: Exploring the Middle Ground Between STRIPS and the Situation Calculus , 1989, KR.

[22]  Donald W. Loveland,et al.  A machine program for theorem-proving , 2011, CACM.

[23]  Jussi Rintanen,et al.  Satisfiability Planning with Constraints on the Number of Actions , 2005, ICAPS.

[24]  Claudette Cayrol,et al.  Using the Davis and Putnam Procedure for an Efficient Computation of Preferred Models , 1996, ECAI.

[25]  Bart Selman,et al.  Planning as Satisfiability , 1992, ECAI.

[26]  Yixin Chen,et al.  Long-distance mutual exclusion for planning , 2009, Artif. Intell..

[27]  Paolo Traverso,et al.  Hierarchical Task Network Planning , 2004 .

[28]  Joost P. Warners,et al.  A Linear-Time Transformation of Linear Inequalities into Conjunctive Normal Form , 1998, Inf. Process. Lett..

[29]  Yixin Chen,et al.  Plan-A : A Cost Optimal Planner Based on SAT-Constrained Optimization ∗ , 2008 .

[30]  V. S. Costa,et al.  Theory and Practice of Logic Programming , 2010 .

[31]  Patrik Haslum,et al.  Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners , 2009, Artif. Intell..

[32]  Matthew L. Ginsberg,et al.  The Complexity of Optimal Planning and a More Efficient Method for Finding Solutions , 2008, ICAPS.

[33]  Hans Tompits,et al.  A framework for compiling preferences in logic programs , 2002, Theory and Practice of Logic Programming.

[34]  Jorge A. Baier,et al.  HTN Planning with Preferences , 2009, IJCAI.

[35]  Yixin Chen,et al.  MaxPlan : Optimal Planning by Decomposed Satisfiability andBackward Reduction , 2006 .

[36]  Ilkka Niemelä,et al.  Unrestricted vs restricted cut in a tableau method for Boolean circuits , 2005, Annals of Mathematics and Artificial Intelligence.

[37]  Subbarao Kambhampati,et al.  Effective Approaches for Partial Satisfaction (Over-Subscription) Planning , 2004, AAAI.

[38]  Ronen I. Brafman,et al.  Introducing Variable Importance Tradeoffs into CP-Nets , 2002, UAI.

[39]  Enrico Pontelli,et al.  Planning with Preferences Using Logic Programming , 2004, LPNMR.

[40]  Enrico Giunchiglia,et al.  Solving satisfiability problems with preferences , 2010, Constraints.

[41]  Michael Gelfond,et al.  Classical negation in logic programs and disjunctive databases , 1991, New Generation Computing.

[42]  Yixin Chen,et al.  Constraint Partitioning for Solving Planning Problems with Trajectory Constraints and Goal Preferences , 2007, IJCAI.

[43]  Bart Selman,et al.  Unifying SAT-based and Graph-based Planning , 1999, IJCAI.

[44]  Ronen I. Brafman,et al.  Planning with Goal Preferences and Constraints , 2005, ICAPS.

[45]  Enrico Giunchiglia,et al.  Planning as Satisfiability with Preferences , 2007, AAAI.

[46]  Niklas Sörensson,et al.  Translating Pseudo-Boolean Constraints into SAT , 2006, J. Satisf. Boolean Model. Comput..

[47]  C. Cordell Green,et al.  Application of Theorem Proving to Problem Solving , 1969, IJCAI.

[48]  S. Kambhampati,et al.  Optiplan: Unifying IP-based and Graph-based Planning , 2005, J. Artif. Intell. Res..

[49]  Michael Wooldridge,et al.  Proceedings of the 21st International Joint Conference on Artificial Intelligence , 2009 .

[50]  Subbarao Kambhampati,et al.  Partial Satisfaction (Over-Subscription) Planning as Heuristic Search , 2004 .

[51]  Subbarao Kambhampati,et al.  Planning as constraint satisfaction: Solving the planning graph by compiling it into CSP , 2001, Artif. Intell..

[52]  Ronen I. Brafman,et al.  On Graphical Modeling of Preference and Importance , 2011, J. Artif. Intell. Res..

[53]  Paul B. Jackson,et al.  Clause Form Conversions for Boolean Circuits , 2004, SAT (Selected Papers.

[54]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[55]  Pedro Barahona,et al.  PSICO: Solving Protein Structures with Constraint Programming and Optimization , 2002, Constraints.

[56]  Stefan Edelkamp,et al.  Optimal Symbolic Planning with Action Costs and Preferences , 2009, IJCAI.

[57]  Olivier Bailleux,et al.  Efficient CNF Encoding of Boolean Cardinality Constraints , 2003, CP.

[58]  David E. Smith Choosing Objectives in Over-Subscription Planning , 2004, ICAPS.

[59]  Paolo Traverso,et al.  Automated planning - theory and practice , 2004 .