Abstractions for Oversubscription Planning

Mirkis, Vitaly; Domshlak, Carmel

doi:10.1609/icaps.v23i1.13545

Cited by 8 publications

(10 citation statements)

References 16 publications

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“…To compare to previous state-ofthe-art approaches to OSP, much work is still needed to adapt these techniques to work in an out-of-the-box fashion. For instance, the planner of Mirkis and Domshlak (2013) requires a specification of variable patterns to be used in their PDB heuristic. Similarly, the approach described in Mirkis and Domshlak (2014) also did not work out-of-thebox, since it is based on the previous one.…”

Section: Comparison To a Baselinementioning

confidence: 99%

“…For instance, the planner of Mirkis and Domshlak (2013) requires a specification of variable patterns to be used in their PDB heuristic. Similarly, the approach described in Mirkis and Domshlak (2014) also did not work out-of-thebox, since it is based on the previous one. However, the performance of these approaches is not too far from the simple blind heuristic, always returning the maximal utility, and therefore we use the blind heuristic as our baseline.…”

Section: Comparison To a Baselinementioning

confidence: 99%

“…In optimal oversubscription planning, little previous work has focused on heuristic search, and progress has been somewhat slower than in the net-benefit setting. A significant performance improvement was first reported by Mirkis and Domshlak (2013). They exploited explicit abstractions (Edelkamp 2001), which were tractable due to their small size.…”

Section: Introductionmentioning

confidence: 99%

“…The approach turned out to work well in practice: in some cases the search space was reduced by three orders of magnitude compared to the baseline algorithm. Later, Mirkis and Domshlak (2014) exploited the notion of landmarks for task reformulation, enriching the task with reachability information. Katz and Mirkis (2016) characterized tractable fragments of oversubscription planning tasks according to causal graph structure and variable domain sizes, and derived admissible estimates from these fragments.…”

Section: Introductionmentioning

confidence: 99%

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Oversubscription Planning as Classical Planning with Multiple Cost Functions

Katz¹,

Keyder

Winterer

et al. 2019

ICAPS

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The aim of classical planning is to minimize the summed cost of operators among those plans that achieve a fixed set of goals. Oversubscription planning (OSP), on the other hand, seeks to maximize the utility of the set of facts achieved by a plan, while keeping the cost of the plan at or below some specified bound. Here, we investigate the use of reformulations that yield planning problems with two separate cost functions, but no utilities, for solving OSP tasks. Such reformulations have also been proposed in the context of netbenefit planning, where the planner tries to maximize the difference between the utility achieved and the cost of the plan. One of our reformulations is adapted directly from that setting, while the other is novel. In both cases, they allow for easy adaptation of existing classical planning heuristics to the OSP problem within a simple branch and bound search. We validate our approach using state of the art admissible heuristics in this framework, and report our results.

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Section: Comparison To a Baselinementioning

confidence: 99%

Section: Comparison To a Baselinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Oversubscription Planning as Classical Planning with Multiple Cost Functions

Katz¹,

Keyder

Winterer

et al. 2019

ICAPS

View full text Add to dashboard Cite

show abstract

“…While maximising the net benefit, b, is the main objective of NBP, it is sometimes combined with a restriction on the plan cost, k, (cf. (Mirkis and Domshlak 2013)) suggesting a multi-objective optimisation of the type we use for COP.…”

Section: Net-benefit Planning Nbp(d)mentioning

confidence: 99%

A Multi-Parameter Complexity Analysis of Cost-Optimal and Net-Benefit Planning

Aghighi

Bäckström

2016

ICAPS

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Aghighi and Bäckström have previously studied cost-optimal planning (COP) and net-benefit planning (NBP) for three action cost domains: the positive integers (Z_+), the non-negative integers (Z_0) and the positive rationals (Q_+). These were indistinguishable under standard complexity analysis for both problems, but separated for COP using parameterised complexity analysis. With the plan cost, k, as parameter, COP was W[2]-complete for Z_+, but para-NP-hard for both Z_0 and Q_+, i.e. presumably much harder. NBP was para-NP-hard for all three domains, thus remaining unseparable. We continue by considering combinations with several additional parameters and also the non-negative rationals (Q_0). Examples of new parameters are the plan length, l, and the largest denominator of the action costs, d. Our findings include: (1) COP remains W[2]-hard for all domains, even if combining all parameters; (2) COP for Z_0 is in W[2] for the combined parameter {k,l}; (3) COP for Q_+ is in W[2] for {k,d} and (4) COP for Q_0 is in W[2] for {k,d,l}. For NBP we consider further additional parameters, where the most crucial one for reducing complexity is the sum of variable utilities. Our results help to understand the previous results, eg. the separation between Z_+ and Q_+ for COP, and to refine the previous connections with empirical findings.

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Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs

Rankooh¹,

Janhunen²

2023

Theory and Practice of Logic Programming

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We establish a novel relation between delete-free planning, an important task for the AI planning community also known as relaxed planning, and logic programming. We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be bijectively captured with stable models of a logic program describing the corresponding relaxed planning problem. We also consider the supported model semantics of logic programs, and introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models. Our experimental results show that these new encodings can provide major performance gain when computing optimal relaxed plans, with our diagnostic encoding outperforming state-of-the-art approaches to relaxed planning regardless of the given time limit when measured on a wide collection of STRIPS planning benchmarks.

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Abstractions for Oversubscription Planning

Cited by 8 publications

References 16 publications

Oversubscription Planning as Classical Planning with Multiple Cost Functions

Oversubscription Planning as Classical Planning with Multiple Cost Functions

A Multi-Parameter Complexity Analysis of Cost-Optimal and Net-Benefit Planning

Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs

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