Anytime heuristic search for partial satisfaction planning

Benton, J.; Do, Minh; Kambhampati, Subbarao

doi:10.1016/j.artint.2008.11.010

Cited by 33 publications

(46 citation statements)

References 11 publications

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“…Metric-FF [37] and then resolve inconsistencies across subproblems. Other planners include Yochan PS [4,5] and mips-xxl [22]. Yochan PS is a heuristic planner based on relaxed graph to obtain a heuristic estimation.…”

Section: Related Workmentioning

confidence: 99%

“…Given that such domains are non-STRIPS, with some ADL [49] constructs used, and that the impact of preferences violation on the plan metric is restricted to be linear (i.e. metric is the sum of weighted preference expressions) (as noted in [5,30]), we have relied on the following compilation technique, similar to the one used in [4,5] to deal with PDDL3 benchmarks inYochan PS and in [27] for dealing with conditional effects: the preferences (goals) in the IPC-5 problems are translated into preconditions of dummy actions, which achieve new dummy literals defining the new problem goals. Then, these new actions can be compiled into STRIPS actions by using an existing tool (we have used both Hoffmann's tool for compiling ADL actions into STRIPS actions, namely adl2strips, and a modification of the same tool used in IPC-5, based on LPG 9 ).…”

Section: Conflicting Soft Goalsmentioning

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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Section: Related Workmentioning

confidence: 99%

Section: Conflicting Soft Goalsmentioning

confidence: 99%

Introducing Preferences in Planning as Satisfiability

Giunchiglia¹,

Maratea²

2010

Journal of Logic and Computation

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“…• net-benefit planning (van den Briel, Sanchez, Do, & Kambhampati, 2004;Sanchez & Kambhampati, 2005;Baier, Bacchus, & McIlraith, 2009;Bonet & Geffner, 2008;Benton, Do, & Kambhampati, 2009;Coles & Coles, 2011;Keyder & Geffner, 2009);…”

Section: Introductionmentioning

confidence: 99%

Deterministic Oversubscription Planning as Heuristic Search: Abstractions and Reformulations

Domshlak

Mirkis

2015

jair

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While in classical planning the objective is to achieve one of the equally attractive goal states at as low total action cost as possible, the objective in deterministic oversubscription planning (OSP) is to achieve an as valuable as possible subset of goals within a fixed allowance of the total action cost. Although numerous applications in various fields share the latter objective, no substantial algorithmic advances have been made in deterministic OSP. Tracing the key sources of progress in classical planning, we identify a severe lack of effective domain-independent approximations for OSP.With our focus here on optimal planning, our goal is to bridge this gap. Two classes of approximation techniques have been found especially useful in the context of optimal classical planning: those based on state-space abstractions and those based on logical landmarks for goal reachability. The question we study here is whether some similar-in-spirit, yet possibly mathematically different, approximation techniques can be developed for OSP. In the context of abstractions, we define the notion of additive abstractions for OSP, study the complexity of deriving effective abstractions from a rich space of hypotheses, and reveal some substantial, empirically relevant islands of tractability. In the context of landmarks, we show how standard goal-reachability landmarks of certain classical planning tasks can be compiled into the OSP task of interest, resulting in an equivalent OSP task with a lower cost allowance, and thus with a smaller search space. Our empirical evaluation confirms the effectiveness of the proposed techniques, and opens a wide gate for further developments in oversubscription planning.

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“…A close related soft goals problem is the Partial Satisfaction one (PSP) [12,1,2] included in the International Planning Competition (IPC) 2006 under the preferences Track and in the IPC 2008 under the net-benefit track. In PSP, nothing prevents, at least a priori, achieving all goals, but there is a trade-off between the utility of achieving a goal and the cost of doing so [1].…”

Section: Introductionmentioning

confidence: 99%

Using the Relaxed Plan Heuristic to Select Goals in Oversubscription Planning Problems

García-Olaya

Rosa

Borrajo

2011

Advances in Artificial Intelligence

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Abstract.Oversubscription planning (OSP) appears in many real problems where finding a plan achieving all goals is infeasible. The objective is to find a feasible plan reaching a goal subset while maximizing some measure of utility. In this paper, we present a new technique to select goals "a priori" for problems in which a cost bound prevents all the goals from being achieved. It uses estimations of distances between goals, which are computed using relaxed plans. Using these distances, a search in the space of subsets of goals is performed, yielding a new set of goals to plan for. A revised planning problem can be created and solved, taking into account only the selected goals. We present experiments in six different domains with good results.

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Anytime heuristic search for partial satisfaction planning

Cited by 33 publications

References 11 publications

Introducing Preferences in Planning as Satisfiability

Introducing Preferences in Planning as Satisfiability

Deterministic Oversubscription Planning as Heuristic Search: Abstractions and Reformulations

Using the Relaxed Plan Heuristic to Select Goals in Oversubscription Planning Problems

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