Martin Wehrle

IJCAI Conference 2021 Conference Paper

On Weak Stubborn Sets in Classical Planning

• Silvan Sievers
• Martin Wehrle

Stubborn sets are a pruning technique for state-space search which is well established in optimal classical planning. In this paper, we show that weak stubborn sets introduced in recent work in planning are actually not weak stubborn sets in Valmari's original sense. Based on this finding, we introduce weak stubborn sets in the original sense for planning by providing a generalized definition analogously to generalized strong stubborn sets in previous work. We discuss the relationship of strong, weak and the previously called weak stubborn sets, thus providing a further step in getting an overall picture of the stubborn set approach in planning.

JAIR Journal 2019 Journal Article

Strong Stubborn Set Pruning for Star-Topology Decoupled State Space Search

• Daniel Gnad
• Jörg Hoffmann
• Martin Wehrle

Analyzing reachability in large discrete transition systems is an important sub-problem in several areas of AI, and of CS in general. State space search is a basic method for conducting such an analysis. A wealth of techniques have been proposed to reduce the search space without affecting the existence of (optimal) solution paths. In particular, strong stubborn set (SSS) pruning is a prominent such method, analyzing action dependencies to prune commutative parts of the search space. We herein show how to apply this idea to star-topology decoupled state space search, a recent search reformulation method invented in the context of classical AI planning. Star-topology decoupled state space search, short decoupled search, addresses planning tasks where a single center component interacts with several leaf components. The search exploits a form of conditional independence arising in this setting: given a fixed path p of transitions by the center, the possible leaf moves compliant with p are independent across the leaves. Decoupled search thus searches over center paths only, maintaining the compliant paths for each leaf separately. This avoids the enumeration of combined states across leaves. Just like standard search, decoupled search is adversely affected by commutative parts of its search space. The adaptation of strong stubborn set pruning is challenging due to the more complex structure of the search space, and the resulting ways in which action dependencies may affect the search. We spell out how to address this challenge, designing optimality-preserving decoupled strong stubborn set (DSSS) pruning methods. We introduce a design for star topologies in full generality, as well as simpler design variants for the practically relevant fork and inverted fork special cases. We show that there are cases where DSSS pruning is exponentially more effective than both, decoupled search and SSS pruning, exhibiting true synergy where the whole is more than the sum of its parts. Empirically, DSSS pruning reliably inherits the best of its components, and sometimes outperforms both.

ICAPS Conference 2019 Conference Paper

Temporal Planning as Refinement-Based Model Checking

• Alexander Heinz
• Martin Wehrle
• Sergiy Bogomolov
• Daniele Magazzeni
• Marius Greitschus
• Andreas Podelski

Planning as model checking based on source-to-source compilations has found increasing attention. Previously proposed approaches for temporal and hybrid planning are based on static translations, in the sense that the resulting model checking problems are uniquely defined by the given input planning problems. As a drawback, the translations can become too large to be efficiently solvable. In this paper, we address propositional temporal planning, lifting static translations to a more flexible framework. Our framework is based on a refinement cycle that allows for adaptively computing suitable translations of increasing size. Our experiments on temporal IPC domains show that the resulting translations to timed automata often become succinct, resulting in promising performance when applied with the directed model checker MCTA.

ICAPS Conference 2019 Conference Paper

Theoretical Foundations for Structural Symmetries of Lifted PDDL Tasks

• Silvan Sievers
• Gabriele Röger
• Martin Wehrle
• Michael Katz 0001

We transfer the notion of structural symmetries to lifted planning task representations, based on abstract structures which we define to model planning tasks. We show that symmetries are preserved by common grounding methods and we shed some light on the relation to previous symmetry concepts used in planning. Using a suitable graph representation of lifted tasks, our experimental analysis of common planning benchmarks reveals that symmetries occur in the lifted representation of many domains. Our work establishes the theoretical ground for exploiting symmetries beyond their previous scope, such as for faster grounding and mutex generation, as well as for state space transformations and reductions.

SoCS Conference 2017 Conference Paper

Strengthening Canonical Pattern Databases with Structural Symmetries

• Silvan Sievers
• Martin Wehrle
• Malte Helmert
• Michael Katz 0001

Symmetry-based state space pruning techniques have proved to greatly improve heuristic search based classical planners. Similarly, abstraction heuristics in general and pattern databases in particular are key ingredients of such planners. However, only little work has dealt with how the abstraction heuristics behave under symmetries. In this work, we investigate the symmetry properties of the popular canonical pattern databases heuristic. Exploiting structural symmetries, we strengthen the canonical pattern databases by adding symmetric pattern databases, making the resulting heuristic invariant under structural symmetry, thus making it especially attractive for symmetry-based pruning search methods. Further, we prove that this heuristic is at least as informative as using symmetric lookups over the original heuristic. An experimental evaluation confirms these theoretical results.

ICAPS Conference 2017 Conference Paper

Stubborn Sets for Fully Observable Nondeterministic Planning

• Dominik Winterer
• Yusra Alkhazraji
• Michael Katz 0001
• Martin Wehrle

Pruning techniques based on strong stubborn sets have recently shown their potential for SAS+ planning as heuristic search. Strong stubborn sets exploit operator independency to safely prune the search space. Like SAS+ planning, fully observable nondeterministic (FOND) planning faces the state explosion problem. However, it is unclear how stubborn set techniques carry over to the nondeterministics setting. In this paper, we introduce stubborn set pruning to FOND planning. We lift the notion of strong stubborn sets and introduce the conceptually more powerful notion of weak stubborn sets to FOND planning. Our experimental analysis shows that weak stubborn sets are beneficial to an LAO* search, and in particular show favorable performance when combined with symmetries and active operator pruning.

ICAPS Conference 2016 Conference Paper

An Analysis of Merge Strategies for Merge-and-Shrink Heuristics

• Silvan Sievers
• Martin Wehrle
• Malte Helmert

The merge-and-shrink framework provides a general basis for the computation of abstraction heuristics for factored transition systems. Recent experimental and theoretical research demonstrated the utility of non-linear merge strategies, which have not been studied in depth. We experimentally analyze the quality of state-of-the-art merge strategies by comparing them to random strategies and with respect to tie-breaking, showing that there is considerable room for improvement. We finally describe a new merge strategy that experimentally outperforms the current state of the art.

IJCAI Conference 2016 Conference Paper

Decoupled Strong Stubborn Sets

• Daniel Gnad
• Martin Wehrle
• J
• ouml; rg Hoffmann

Recent work has introduced fork-decoupled search, addressing classical planning problems where a single center component provides preconditions for several leaf components. Given a fixed center path π C, the leaf moves compliant with π C can then be scheduled independently for each leaf. Fork-decoupled search thus searches over center paths only, maintaining the compliant paths for each leaf separately. This can yield dramatic benefits. It is empirically complementary to partial order reduction via strong stubborn sets, in that each method yields its strongest reductions in different benchmarks. Here we show that the two methods can be combined, in the form of strong stubborn sets for fork-decoupled search. This can yield exponential advantages relative to both methods. Empirically, the combination reliably inherits the best of its components, and often outperforms both.

IJCAI Conference 2016 Conference Paper

Graph-Based Factorization of Classical Planning Problems

• Martin Wehrle
• Silvan Sievers
• Malte Helmert

In domain-independent planning, dependencies of operators and variables often prevent the effective application of planning techniques that rely on loosely coupled problems (like factored planning or partial order reduction). In this paper, we propose a generic approach for factorizing a classical planning problem into an equivalent problem with fewer operator and variable dependencies. Our approach is based on variable factorization, which can be reduced to the well-studied problem of graph factorization. While the state spaces of the original and the factorized problems are isomorphic, the factorization offers the potential to exponentially reduce the complexity of planning techniques like factored planning and partial order reduction.

SoCS Conference 2016 Conference Paper

Sleep Sets Meet Duplicate Elimination

• Yusra Alkhazraji
• Martin Wehrle

The sleep sets technique is a path-dependent pruning method for state space search. In the past, the combination of sleep sets with graph search algorithms that perform duplicate elimination has often shown to be error-prone. In this paper, we provide the theoretical basis for the integration of sleep sets with common search algorithms in AI that perform duplicate elimination. Specifically, we investigate approaches to safely integrate sleep sets with optimal (best-first) search algorithms. Based on this theory, we provide an initial step towards integrating sleep sets within A* and additional state pruning techniques like strong stubborn sets. Our experiments show slight, yet consistent improvements on the number of generated search nodes across a large number of standard domains from the international planning competitions.

IJCAI Conference 2016 Conference Paper

Structural Symmetries for Fully Observable Nondeterministic Planning

• Dominik Winterer
• Martin Wehrle
• Michael Katz

Symmetry reduction has significantly contributed to the success of classical planning as heuristic search. However, it is an open question if symmetry reduction techniques can be lifted to fully observable nondeterministic (FOND) planning. We generalize the concepts of structural symmetries and symmetry reduction to FOND planning and specifically to the LAO* algorithm. Our base implementation of LAO* in the Fast Downward planner is competitive with the LAO*-based FOND planner myND. Our experiments further show that symmetry reduction can yield strong performance gains compared to our base implementation of LAO*.

AAAI Conference 2015 Conference Paper

A Generalization of Sleep Sets Based on Operator Sequence Redundancy

• Robert C. Holte
• Yusra Alkhazraji
• Martin Wehrle

Pruning techniques have recently been shown to speed up search algorithms by reducing the branching factor of large search spaces. One such technique is sleep sets, which were originally introduced as a pruning technique for model checking, and which have recently been investigated on a theoretical level for planning. In this paper, we propose a generalization of sleep sets and prove its correctness. While the original sleep sets were based on the commutativity of operators, generalized sleep sets are based on a more general notion of operator sequence redundancy. As a result, our approach dominates the original sleep sets variant in terms of pruning power. On a practical level, our experimental evaluation shows the potential of sleep sets and their generalizations on a large and common set of planning benchmarks.

AAAI Conference 2015 Conference Paper

Factored Symmetries for Merge-and-Shrink Abstractions

• Silvan Sievers
• Martin Wehrle
• Malte Helmert
• Alexander Shleyfman
• Michael Katz

Merge-and-shrink heuristics crucially rely on effective reduction techniques, such as bisimulation-based shrinking, to avoid the combinatorial explosion of abstractions. We propose the concept of factored symmetries for merge-andshrink abstractions based on the established concept of symmetry reduction for state-space search. We investigate under which conditions factored symmetry reduction yields perfect heuristics and discuss the relationship to bisimulation. We also devise practical merging strategies based on this concept and experimentally validate their utility.

AAAI Conference 2015 Conference Paper

Heuristics and Symmetries in Classical Planning

• Alexander Shleyfman
• Michael Katz
• Malte Helmert
• Silvan Sievers
• Martin Wehrle

Heuristic search is a state-of-the-art approach to classical planning. Several heuristic families were developed over the years to automatically estimate goal distance information from problem descriptions. Orthogonally to the development of better heuristics, recent years have seen an increasing interest in symmetry-based state space pruning techniques that aim at reducing the search effort. However, little work has dealt with how the heuristics behave under symmetries. We investigate the symmetry properties of existing heuristics and reveal that many of them are invariant under symmetries.

SoCS Conference 2015 Conference Paper

Improved Pattern Selection for PDB Heuristics in Classical Planning (Extended Abstract)

• Sascha Scherrer
• Florian Pommerening
• Martin Wehrle

The iPDB approach selects patterns by a local search in the space of pattern collections. This search often gets stuck in local optima, which limits the quality of the resulting heuristic. In this research abstract, we report on current progress to tackle this problem. We investigate variable neighborhood search with encouraging experimental results.

IJCAI Conference 2015 Conference Paper

Integrating Partial Order Reduction and Symmetry Elimination for Cost-Optimal Classical Planning

• Martin Wehrle
• Malte Helmert
• Alexander Shleyfman
• Michael Katz

Pruning techniques based on partial order reduction and symmetry elimination have recently found increasing attention for optimal planning. Although these techniques appear to be rather different, they base their pruning decisions on similar ideas from a high level perspective. In this paper, we propose safe integrations of partial order reduction and symmetry elimination for cost-optimal classical planning. We show that previously proposed symmetrybased search algorithms can safely be applied with strong stubborn sets. In addition, we derive the notion of symmetrical strong stubborn sets as a more tightly integrated concept. Our experiments show the potential of our approaches.

ICAPS Conference 2015 Conference Paper

PDDL+ Planning with Hybrid Automata: Foundations of Translating Must Behavior

• Sergiy Bogomolov
• Daniele Magazzeni
• Stefano Minopoli
• Martin Wehrle

Planning in hybrid domains poses a special challenge due to the involved mixed discrete-continuous dynamics. A recent solving approach for such domains is based on applying model checking techniques on a translation of PDDL+ planning problems to hybrid automata. However, the proposed translation is limited because must behavior is only over-approximated, and hence, processes and events are not reflected exactly. In this paper, we present the theoretical foundation of an exact PDDL+ translation. We propose a schema to convert a hybrid automaton with must transitions into an equivalent hybrid automaton featuring only may transitions.

ECAI Conference 2014 Conference Paper

Bounded Intention Planning Revisited

• Silvan Sievers
• Martin Wehrle
• Malte Helmert

ICAPS Conference 2014 Conference Paper

Efficient Stubborn Sets: Generalized Algorithms and Selection Strategies

• Martin Wehrle
• Malte Helmert

Strong stubborn sets have recently been analyzed and successfully applied as a pruning technique for planning as heuristic search. Strong stubborn sets are defined declaratively as constraints over operator sets. We show how these constraints can be relaxed to offer more freedom in choosing stubborn sets while maintaining the correctness and optimality of the approach. In general, many operator sets satisfy the definition of stubborn sets. We study different strategies for selecting among these possibilities and show that existing approaches can be considerably improved by rather simple strategies, eliminating most of the overhead of the previous state of the art.

AAAI Conference 2014 Conference Paper

Generalized Label Reduction for Merge-and-Shrink Heuristics

• Silvan Sievers
• Martin Wehrle
• Malte Helmert

Label reduction is a technique for simplifying families of labeled transition systems by dropping distinctions between certain transition labels. While label reduction is critical to the efficient computation of merge-and-shrink heuristics, current theory only permits reducing labels in a limited number of cases. We generalize this theory so that labels can be reduced in every intermediate abstraction of a merge-andshrink tree. This is particularly important for efficiently computing merge-and-shrink abstractions based on non-linear merge strategies. As a case study, we implement a nonlinear merge strategy based on the original work on mergeand-shrink heuristics in model checking by Dräger et al.

AAAI Conference 2014 Conference Paper

Planning as Model Checking in Hybrid Domains

• Sergiy Bogomolov
• Daniele Magazzeni
• Andreas Podelski
• Martin Wehrle

Planning in hybrid domains is an important and challenging task, and various planning algorithms have been proposed in the last years. From an abstract point of view, hybrid planning domains are based on hybrid automata, which have been studied intensively in the model checking community. In particular, powerful model checking algorithms and tools have emerged for this formalism. However, despite the quest for more scalable planning approaches, model checking algorithms have not been applied to planning in hybrid domains so far. In this paper, we make a first step in bridging the gap between these two worlds. We provide a formal translation scheme from PDDL+ to the standard formalism of hybrid automata, as a solid basis for using hybrid system modelchecking tools for dealing with hybrid planning domains. As a case study, we use the SpaceEx model checker, showing how we can address PDDL+ domains that are out of the scope of state-of-the-art planners.

AAAI Conference 2014 Conference Paper

Symbolic Domain Predictive Control

• Johannes Löhr
• Martin Wehrle
• Maria Fox
• Bernhard Nebel

Planning-based methods to guide switched hybrid systems from an initial state into a desired goal region opens an interesting field for control. The idea of the Domain Predictive Control (DPC) approach is to generate input signals affecting both the numerical states and the modes of the system by stringing together atomic actions to a logically consistent plan. However, the existing DPC approach is restricted in the sense that a discrete and pre-defined input signal is required for each action. In this paper, we extend the approach to deal with symbolic states. This allows for the propagation of reachable regions of the state space emerging from actions with inputs that can be arbitrarily chosen within specified input bounds. This symbolic extension enables the applicability of DPC to systems with bounded inputs sets and increases its robustness due to the implicitly reduced search space. Moreover, precise numeric goal states instead of goal regions become reachable.

ICAPS Conference 2014 Conference Paper

Under-Approximation Refinement for Classical Planning

• Manuel Heusner
• Martin Wehrle
• Florian Pommerening
• Malte Helmert

A general and important problem of search-based planning techniques is the state explosion problem, which is usually tackled with approaches to reduce the branching factor of the planning task. Such approaches often implicitly exploit the observation that the number of available operators is higher than the number of operators that are actually needed to find a plan. In this paper, we propose a simple, but general under-approximation refinement framework for satisficing planning that explicitly exploits this observation. Our approach iteratively searches for plans with operator subsets, which are refined if necessary by adding operators that appear to be needed. Our evaluation shows that even a straight-forward instantiation of this framework yields a competitive planner that often finds plans with small operator sets.

ICAPS Conference 2013 Conference Paper

The Relative Pruning Power of Strong Stubborn Sets and Expansion Core

• Martin Wehrle
• Malte Helmert
• Yusra Alkhazraji
• Robert Mattmüller

In the last years, pruning techniques based on partial order reduction have found increasing attention in the planning community. One recent result is that the expansion core method is a special case of the strong stubborn sets method proposed in model checking. However, it is still an open question if there exist efficiently computable strong stubborn sets with strictly higher pruning power than expansion core. In this paper, we prove that the pruning power of strong stubborn sets is strictly higher than the pruning power of expansion core even for a straight-forward instantiation of strong stubborn sets. This instantiation is as efficiently computable as expansion core. Hence, our theoretical results suggest that strong stubborn sets should be preferred to expansion core. Our empirical evaluation on all optimal benchmarks from the international planning competitions up to 2011 supports the theoretical results.

ECAI Conference 2012 Conference Paper

A Stubborn Set Algorithm for Optimal Planning

• Yusra Alkhazraji
• Martin Wehrle
• Robert Mattmüller
• Malte Helmert

We adapt a partial order reduction technique based on stubborn sets, originally proposed for detecting dead ends in Petri Nets, to the setting of optimal planning. We demonstrate that stubborn sets can provide significant state space reductions on standard planning benchmarks, outperforming the expansion core method.

ICAPS Conference 2012 Conference Paper

About Partial Order Reduction in Planning and Computer Aided Verification

• Martin Wehrle
• Malte Helmert

Partial order reduction is a state space pruning approach that has been originally introduced in computer aided verification. Recently, various partial order reduction techniques have also been proposed for planning. Despite very similar underlying ideas, the relevant literature from computer aided verification has hardly been analyzed in the planning area so far, and it is unclear how these techniques are formally related. We provide an analysis of existing partial order reduction techniques and their relationships. We show that recently proposed approaches in planning are instances of general partial order reduction approaches from computer aided verification. Our analysis reveals a hierarchy of dominance relationships and shows that there is still room for improvement for partial order reduction techniques in planning. Overall, we provide a first step towards a better understanding and a unifying theory of partial order reduction techniques from different areas.

ICAPS Conference 2008 Conference Paper

Useless Actions Are Useful

• Martin Wehrle
• Sebastian Kupferschmid
• Andreas Podelski

Planning as heuristic search is a powerful approach to solving domain independent planning problems. In recent years, various successful heuristics and planners like FF, LPG, Fast Downward or SGPlan have been proposed in this context. However, as heuristics only estimate the distance to goal states, a general problem of heuristic search is the existence of plateaus in the search space topology which can cause the search process to degenerate. Additional techniques like helpful actions or preferred operators that evaluate the "usefulness" of actions are often successful strategies to support the search in such situations. In this paper, we introduce a general method to evaluate the usefulness of actions. We propose a technique to enhance heuristic search by identifying "useless" actions that are not needed to find optimal plans. In contrast to helpful actions or preferred operators that are specific to the FF and Causal Graph heuristic, respectively, our method can be combined with arbitrary heuristics. We show that this technique often yields significant performance improvements.