Star-topology decoupled state space search

Gnad, Daniel; Hoffmann, Jörg

doi:10.1016/j.artint.2017.12.004

Cited by 19 publications

(36 citation statements)

References 51 publications

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“…As work in the planning domain has already shown, star-topology decoupling is orthogonal to, and may have exponential advantages over, partial-order reduction, symmetry breaking, symbolic representations, and heuristic search. It can also be fruitfully combined with all of these [18,17,16,15].…”

Section: Resultsmentioning

confidence: 99%

“…We give a brief outline and refer to Gnad and Hoffmann [15] for details. In Section 2.2, we prove correctness of star-topology decoupling for reachability checking.…”

Section: Star-topology Decouplingmentioning

confidence: 99%

“…Star-topology decoupling can have exponential advantages over other partial-order reduction methods. Consider the following excerpt of Gnad and Hoffmann's [15] [39]). Unf: unfolding (using Cunf [34] given the presence of read arcs).…”

Section: Introductionmentioning

confidence: 99%

“…Here we adapt a new method from AI Planning, star-topology decoupling [14,15], to model checking. Contrary to other methods developed in AI, which typically aim at finding solution paths quickly, the major strength of star-topology decoupling lies in proving unreachability: in a model checking setting, verifying correctness of safety properties.…”

Section: Introductionmentioning

confidence: 99%

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Star-Topology Decoupling in SPIN

Gnad

Dubbert

Lafuente

et al. 2018

Model Checking Software

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Abstract. Star-topology decoupling is a state space search method recently introduced in AI Planning. It decomposes the input model into components whose interaction structure has a star shape. The decoupled search algorithm enumerates transition paths only for the center component, maintaining the leaf-component state space separately for each leaf. This is a form of partial-order reduction, avoiding interleavings across leaf components. It can, and often does, have exponential advantages over stubborn set pruning and unfolding. AI Planning relates closely to model checking of safety properties, so the question arises whether decoupled search can be successful in model checking as well. We introduce a first implementation of star-topology decoupling in SPIN, where the center maintains global variables while the leaves maintain local ones. Preliminary results on several case studies attest to the potential of the approach.

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Section: Resultsmentioning

confidence: 99%

“…We give a brief outline and refer to Gnad and Hoffmann [15] for details. In Section 2.2, we prove correctness of star-topology decoupling for reachability checking.…”

Section: Star-topology Decouplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Star-Topology Decoupling in SPIN

Gnad

Dubbert

Lafuente

et al. 2018

Model Checking Software

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“…Factored planning methods (e.g., Amir & Engelhardt, 2003;Domshlak & Brafman, 2006;Fabre, Jezequel, Haslum, & Thiébaux, 2010) decompose the problem into parts that are all classical, but may still distinguish one of those as primary (Gnad & Hoffmann, 2018). Most planners with semantic attachments do not implement an analogue of Bender's cuts, i.e., inferring constraints on the primary model from inconsistencies or suboptimal solutions to the subproblem, though there are exceptions, e.g., the ITSAT temporal planner, which infers causal constraints from inconsistencies in temporal constraints (Rankooh & Ghassem-Sani, 2015).…”

Section: Special-purpose Solvers In Heuristic Search Planningmentioning

confidence: 99%

Extending Classical Planning with State Constraints: Heuristics and Search for Optimal Planning

Haslum¹,

Ivankovic²,

Ramírez³

et al. 2018

jair

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We present a principled way of extending a classical AI planning formalism with systems of state constraints, which relate -sometimes determine -the values of variables in each state traversed by the plan. This extension occupies an attractive middle ground between expressivity and complexity. It enables modelling a new range of problems, as well as formulating more efficient models of classical planning problems. An example of the former is planning-based control of networked physical systems -power networks, for examplein which a local, discrete control action can have global effects on continuous quantities, such as altering flows across the entire network. At the same time, our extension remains decidable as long as the satisfiability of sets of state constraints is decidable, including in the presence of numeric state variables, and we demonstrate that effective techniques for costoptimal planning known in the classical setting -in particular, relaxation-based admissible heuristics -can be adapted to the extended formalism. In this paper, we apply our approach to constraints in the form of linear or non-linear equations over numeric state variables, but the approach is independent of the type of state constraints, as long as there exists a procedure that decides their consistency. The planner and the constraint solver interact through a well-defined, narrow interface, in which the solver requires no specialisation to the planning context. Furthermore, we present an admissible search algorithm -a variant of A -that is able to make use of additional information provided by the search heuristic, in the form of preferred actions. Although preferred actions have been widely used in satisficing planning, we are not aware of any previous use of them in optimal planning.

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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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Star-topology decoupled state space search

Cited by 19 publications

References 51 publications

Star-Topology Decoupling in SPIN

Star-Topology Decoupling in SPIN

Extending Classical Planning with State Constraints: Heuristics and Search for Optimal Planning

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

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