Long-distance mutual exclusion for planning

Chen, Yixin; Huang, Ruoyun; Zhao, Xiaoliang; Zhang, Weixiong

doi:10.1016/j.artint.2008.11.004

Cited by 28 publications

(35 citation statements)

References 17 publications

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“…Indeed, according to Kautz and Selman (Kautz & Selman, 1999;Long et al, 2000), such CNF encodings-in particular the mutex relations computed by Graphplan-are vital to SATPLAN's performance. While recent results on more effective encodings may challenge this assessment (Rintanen, Heljanko, & Niemelä, 2006;Chen, Huang, Xing, & Zhang, 2009;Robinson, Gretton, Pham, & Sattar, 2008), even so the graphplan-based encodings are of interest simply because they have been widely used during almost a decade. It remains of course an important question whether and to what extent our results carry over to alternative CNF encodings.…”

Section: Summary Of Theoretical Resultsmentioning

confidence: 99%

“…Hence, for these encodings, it seems that proving our results might indeed be comparatively easy. A more challenging subject may be some more recent developments, such as the encodings by Rintanen et al (2006) which often give substantial speed-ups through novel notions of parallelity, the encodings by Chen et al (2009) which introduce long-distance mutex relations, or the encodings by Robinson et al (2008) which make use of effective operator splitting and factoring methods.…”

Section: Resultsmentioning

confidence: 99%

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Friends or Foes? On Planning as Satisfiability and Abstract CNF Encodings

Domshlak¹,

Hoffmann²,

Sabharwal³

2009

jair

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Planning as satisfiability, as implemented in, for instance, the SATPLAN tool, is a highly competitive method for finding parallel step-optimal plans. A bottleneck in this approach is to prove the absence of plans of a certain length. Specifically, if the optimal plan has n steps, then it is typically very costly to prove that there is no plan of length n−1. We pursue the idea of leading this proof within solution length preserving abstractions (overapproximations) of the original planning task. This is promising because the abstraction may have a much smaller state space; related methods are highly successful in model checking. In particular, we design a novel abstraction technique based on which one can, in several widely used planning benchmarks, construct abstractions that have exponentially smaller state spaces while preserving the length of an optimal plan. Surprisingly, the idea turns out to appear quite hopeless in the context of planning as satisfiability. Evaluating our idea empirically, we run experiments on almost all benchmarks of the international planning competitions up to IPC 2004, and find that even hand-made abstractions do not tend to improve the performance of SATPLAN. Exploring these findings from a theoretical point of view, we identify an interesting phenomenon that may cause this behavior. We compare various planning-graph based CNF encodings φ of the original planning task with the CNF encodings φ σ of the abstracted planning task. We prove that, in many cases, the shortest resolution refutation for φ σ can never be shorter than that for φ. This suggests a fundamental weakness of the approach, and motivates further investigation of the interplay between declarative transition-systems, over-approximating abstractions, and SAT encodings.

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Section: Summary Of Theoretical Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Friends or Foes? On Planning as Satisfiability and Abstract CNF Encodings

Domshlak¹,

Hoffmann²,

Sabharwal³

2009

jair

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“…MaxPlan [14,16,61]. The question is whether the new features we have proposed can be simply integrated into MaxPlan.…”

Section: Related Workmentioning

confidence: 99%

“…This work is an extension of the first attempt based on SAT, which can effectively deal with this important problem [34]: it is actually considered the reference SAT-based approach to deal with preferences, see e.g. [30], the AAAI 2007 tutorial on 'Planning and Scheduling with Over-Subscribed Resources, Preferences, and Soft Constraints' 13 by Do, Zimmermann and Kambhampati, and the joint KR/ICAPS 2008 tutorial on 'Preferences, Planning and Control' 14 by Brafman. Our experimental analysis points out that with a few preferences (as it is the case for problems with soft goals), the performances in term of CPU time of satplan(w)/(b) are roughly the same as satplan's ones, and that the same happens for satplan(w) even in the presence of thousands of preferences, at least on some domains: significant degradations only show up in a reduced portion of the analysed instances (results are even better for satplan(s): it solves a different problem but often its results are the same of satplan(w), and thus it can be used to compute good approximated results).…”

Section: Conclusion Final Remarks and Future Workmentioning

confidence: 99%

“…Planning as Satisfiability [42] is one of the most well-known and effective techniques for classical planning: satplan [43,44] is a planner based on propositional satisfiability (SAT) and, considering the track for optimal propositional planner, it has been the winning system in the 4th International Planning Competition (IPC) 1 [38] and a co-winner in the IPC-5 2 [30] (together with another planner based on SAT, MaxPlan [14,16,61]). Given a planning problem Π, the basic idea of Planning as Satisfiability (a.k.a.…”

Section: Introductionmentioning

confidence: 99%

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Introducing Preferences in Planning as Satisfiability

Giunchiglia¹,

Maratea²

2010

Journal of Logic and Computation

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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 Π n 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.

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A Comparison of h 2 and MMM for Mutex Pair Detection Applied to Pattern Databases

Sadeqi

Holte

Zilles

2014

Advances in Artificial Intelligence

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Abstract. In state space search or planning, a pair of variable-value assignments that does not occur in any reachable state is considered a mutually exclusive (mutex) pair. To improve the efficiency of planners, the problem of detecting such pairs has been addressed frequently in the planning literature. No known efficient method for detecting mutex pairs is able to find all such pairs. Hence, the number and type of mutex constraints detected by various algorithms are different from one another. The purpose of this paper is to study the effects on search performance when errors are made by the mutex detection method that is informing the construction of a pattern database (PDB). PDBs are deployed for creating heuristic functions that are then used to guide search. We consider two mutex detection methods, h 2 , which can fail to recognize a mutex pair but never regards a reachable pair as mutex, and the sampling-based method MMM, which makes the opposite type of error. Both methods are very often perfect, i.e. they exactly identify which pairs are mutex and which are reachable. In the cases that they err that we examine in this paper, h 2 's errors cause search to be moderately slower (7%−24%) whereas MMM's errors have very little effect on search speed or suboptimality, even when its sample size is quite small.

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Long-distance mutual exclusion for planning

Cited by 28 publications

References 17 publications

Friends or Foes? On Planning as Satisfiability and Abstract CNF Encodings

Friends or Foes? On Planning as Satisfiability and Abstract CNF Encodings

Introducing Preferences in Planning as Satisfiability

A Comparison of h 2 and MMM for Mutex Pair Detection Applied to Pattern Databases

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