Synthesising Succinct Strategies in Safety and Reachability Games

Geeraerts, Gilles; Goossens, Joël; Stainer, Amélie

doi:10.1007/978-3-319-11439-2_8

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Otherwise, another optimisation of the algorithm occurs: v is discarded from ToSearch, as well as all the nodes that are smaller than v (line 10). This is correct by properties of the tba-simulation (see [GGS14]), and allows the algorithm to eliminate candidates from ToSearch much quicker.…”

Section: Computation Of Upre # (A)mentioning

confidence: 83%

A Backward Algorithm for the Multiprocessor Online Feasibility of Sporadic Tasks

Geeraerts

Goossens

Nguyen

2017

2017 17th International Conference on Application of Concurrency to System Design (ACSD)

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The online feasibility problem (for a set of sporadic tasks) asks whether there is a scheduler that always prevents deadline misses (if any), whatever the sequence of job releases, which is a priori unknown to the scheduler. In the multiprocessor setting, this problem is notoriously difficult. The only exact test for this problem has been proposed by Bonifaci and Marchetti-Spaccamela: it consists in modelling all the possible behaviours of the scheduler and of the tasks as a graph; and to interpret this graph as a game between the tasks and the scheduler, which are seen as antagonistic players. Then, computing a correct scheduler is equivalent to finding a winning strategy for the 'scheduler player', whose objective in the game is to avoid deadline misses. In practice, however this approach is limited by the intractable size of the graph. In this work, we consider the classical attractor algorithm to solve such games, and introduce antichain techniques to optimise its performance in practice and overcome the huge size of the game graph. These techniques are inspired from results from the formal methods community, and exploit the specific structure of the feasibility problem. We demonstrate empirically that our approach allows to dramatically improve the performance of the game solving algorithm. * This work has been supported by the F.R.S./FNRS PDR grant FORESt: Formal verifica-tiOn techniques for REal-time Scheduling problems.

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Section: Computation Of Upre # (A)mentioning

confidence: 83%

A Backward Algorithm for the Multiprocessor Online Feasibility of Sporadic Tasks

Geeraerts

Goossens

Nguyen

2017

2017 17th International Conference on Application of Concurrency to System Design (ACSD)

Self Cite

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“…In this step, one can provide a heuristic that removes certain states that do not contribute to the computation. We provide an optional step that detects surely losing states, as presented in [14].…”

Section: Preprocessing Of the Automaton (Line 1)mentioning

confidence: 99%

Acacia-Bonsai: A Modern Implementation of Downset-Based LTL Realizability

Cadilhac

Pérez

2023

Lecture Notes in Computer Science

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We describe our implementation of downset-manipulating algorithms used to solve the realizability problem for linear temporal logic (LTL). These algorithms were introduced by Filiot et al. in the 2010s and implemented in the tools Acacia and Acacia+ in C and Python. We identify degrees of freedom in the original algorithms and provide a complete rewriting of Acacia in C++20 articulated around genericity and leveraging modern techniques for better performance. These techniques include compile-time specialization of the algorithms, the use of SIMD registers to store vectors, and several preprocessing steps, some relying on efficient Binary Decision Diagram (BDD) libraries. We also explore different data structures to store downsets. The resulting tool is competitive against comparable modern tools.

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Section: Preprocessing Of the Automaton (Line 1)mentioning

confidence: 99%

Acacia-Bonsai: A Modern Implementation of Downset-Based LTL Realizability

Cadilhac¹,

Pérez²

2022

Preprint

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We describe our implementation of downset-manipulating algorithms used to solve the realizability problem for linear temporal logic (LTL). These algorithms were introduced by Filiot et al. in the 2010s and implemented in the tools Acacia and Acacia+ in C and Python. We identify degrees of freedom in the original algorithms and provide a complete rewriting of Acacia in C++20 articulated around genericity and leveraging modern techniques for better performances. These techniques include compile-time specialization of the algorithms, the use of SIMD registers to store vectors, and several preprocessing steps, some relying on efficient Binary Decision Diagram (BDD) libraries. We also explore different data structures to store downsets. The resulting tool is competitive against comparable modern tools.

show abstract

Synthesising Succinct Strategies in Safety and Reachability Games

Cited by 4 publications

References 15 publications

A Backward Algorithm for the Multiprocessor Online Feasibility of Sporadic Tasks

A Backward Algorithm for the Multiprocessor Online Feasibility of Sporadic Tasks

Acacia-Bonsai: A Modern Implementation of Downset-Based LTL Realizability

Acacia-Bonsai: A Modern Implementation of Downset-Based LTL Realizability

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