On the Relation between Star-Topology Decoupling and Petri Net Unfolding

Gnad, Daniel; Hoffmann, Jörg

doi:10.1609/icaps.v29i1.3473

Cited by 3 publications

References 13 publications

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Model Checking $$\omega $$-Regular Properties with Decoupled Search

Gnad

Eisenhut

Lafuente

et al. 2021

Computer Aided Verification

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Decoupled search is a state space search method originally introduced in AI Planning. Similar to partial-order reduction methods, decoupled search exploits the independence of components to tackle the state explosion problem. Similar to symbolic representations, it does not construct the explicit state space, but sets of states are represented in a compact manner, exploiting component independence. Given the success of both partial-order reduction and symbolic representations when model checking liveness properties, our goal is to add decoupled search to the toolset of liveness checking methods. Specifically, we show how decoupled search can be applied to liveness verification for composed Büchi automata by adapting, and showing correct, a standard algorithm for detecting lassos (i.e., infinite accepting runs), namely nested depth-first search. We evaluate our approach using a prototype implementation.

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Model Checking $$\omega $$-Regular Properties with Decoupled Search

Gnad

Eisenhut

Lafuente

et al. 2021

Computer Aided Verification

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Revisiting Dominance Pruning in Decoupled Search

Gnad

2021

AAAI

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In classical planning as search, duplicate state pruning is a standard method to avoid unnecessarily handling the same state multiple times. In decoupled search, similar to symbolic search approaches, search nodes, called decoupled states, do not correspond to individual states, but to sets of states. Therefore, duplicate state pruning is less effective in decoupled search, and dominance pruning is employed, taking into account the state sets. We observe that the time required for dominance checking dominates the overall runtime, and propose two ways to tackle this issue. Our main contribution is a stronger variant of dominance checking for optimal planning, where efficiency and pruning power are most crucial. The new variant greatly improves the latter, without incurring a computational overhead. Moreover, we develop three methods that make the dominance check more efficient: exact duplicate checking, which, albeit resulting in weaker pruning, can pay off due to the use of hashing; avoiding the dominance check in non-optimal planning if leaf state spaces are invertible; and exploiting the transitivity of the dominance relation to only check against the relevant subset of visited decoupled states. We show empirically that all our improvements are indeed beneficial in many standard benchmarks.

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

Gnad¹,

Hoffmann²,

Wehrle³

2019

jair

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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.

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On the Relation between Star-Topology Decoupling and Petri Net Unfolding

Cited by 3 publications

References 13 publications

Model Checking $$\omega $$-Regular Properties with Decoupled Search

Model Checking $$\omega $$-Regular Properties with Decoupled Search

Revisiting Dominance Pruning in Decoupled Search

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

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