Quantitative Assume Guarantee Synthesis

Almagor, Shaull; Kupferman, Orna; Ringert, Jan Oliver; Velner, Yaron

doi:10.1007/978-3-319-63390-9_19

Cited by 15 publications

(21 citation statements)

References 29 publications

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“…Using (36) and the previous derivation we see that for every system state q ∈ Q 1 ∩ a Z ∞ with ab rank(q) = (i, j) holds (a)δ 1 (q) ⊆ a Z ∞ and for all q ′ ∈δ 1…”

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confidence: 81%

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Environmentally-Friendly GR(1) Synthesis

Majumdar

Piterman

Schmuck

2019

Lecture Notes in Computer Science

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Many problems in reactive synthesis are stated using two formulas -an environment assumption and a system guarantee-and ask for an implementation that satisfies the guarantee in environments that satisfy their assumption. Reactive synthesis tools often produce strategies that formally satisfy such specifications by actively preventing an environment assumption from holding. While formally correct, such strategies do not capture the intention of the designer. We introduce an additional requirement in reactive synthesis, non-conflictingness, which asks that a system strategy should always allow the environment to fulfill its liveness requirements. We give an algorithm for solving GR(1) synthesis that produces non-conflicting strategies. Our algorithm is given by a 4-nested fixed point in the µ-calculus, in contrast to the usual 3-nested fixed point for GR(1). Our algorithm ensures that, in every environment that satisfies its assumptions on its own, traces of the resulting implementation satisfy both the assumptions and the guarantees. In addition, the asymptotic complexity of our algorithm is the same as that of the usual GR(1) solution. We have implemented our algorithm and show how its performance compares to the usual GR(1) synthesis algorithm.

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“…Using (36) and the previous derivation we see that for every system state q ∈ Q 1 ∩ a Z ∞ with ab rank(q) = (i, j) holds (a)δ 1 (q) ⊆ a Z ∞ and for all q ′ ∈δ 1…”

mentioning

confidence: 81%

“…Algorithmic Solution for GR(1) Winning ConditionsWe now consider a general GR(1) game (H, F A , F G ) with F A = { 1 F A , . .…”

mentioning

confidence: 99%

Environmentally-Friendly GR(1) Synthesis

Majumdar

Piterman

Schmuck

2019

Lecture Notes in Computer Science

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“…Similarly, extensions of the classical temporal logics LTL and CTL with quantitative semantics have been studied in different contexts, with discounting [33,2], averaging [14,17], or richer constructs [13,3]. In contrast, the study of quantitative temporal logics for strategic reasoning has remained rather limited: works on LTL[F ] include algorithms for synthesis and rational synthesis [3,4,5,6], but no logic combines the quantitative aspect of LTL[F ] with the strategic reasoning offered by SL. Thus, to the best of our knowledge, our model-checking algorithm for SL[F ] is the first decidability result for a quantitative extension of a strategic specification formalism (without restricting to bounded-memory strategies).…”

Section: Related Workmentioning

confidence: 99%

Reasoning about Quality and Fuzziness of Strategic Behaviours

Bouyer

Kupferman

Markey

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Temporal logics are extensively used for the specification of on-going behaviours of reactive systems. Two significant developments in this area are the extension of traditional temporal logics with modalities that enable the specification of on-going strategic behaviours in multi-agent systems, and the transition of temporal logics to a quantitative setting, where different satisfaction values enable the specifier to formalise concepts such as certainty or quality. We introduce and study SL[F]-a quantitative extension of SL (Strategy Logic), one of the most natural and expressive logics describing strategic behaviours. The satisfaction value of an SL[F] formula is a real value in [0, 1], reflecting "how much" or "how well" the strategic on-going objectives of the underlying agents are satisfied. We demonstrate the applications of SL[F] in quantitative reasoning about multi-agent systems, by showing how it can express concepts of stability in multi-agent systems, and how it generalises some fuzzy temporal logics. We also provide a model-checking algorithm for our logic, based on a quantitative extension of Quantified CTL ⋆ .

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“…The requirement can be applied to a threshold value or to all values v ∈ [0, 1]. For example, our autonomous car controller has to achieve satisfaction value 1 in roads with no simultaneous or successive occurrences of safe and obs, and value 3 4 in roads that violate the latter only with some obs followed by safe. We then argue that the situation is similar to that of high-quality assume guarantee synthesis [3], where richer relations between a quantitative assumption and a quantitative guarantee are of interest.…”

Section: Introductionmentioning

confidence: 99%

“…For example, our autonomous car controller has to achieve satisfaction value 1 in roads with no simultaneous or successive occurrences of safe and obs , and value in roads that violate the latter only with some obs followed by safe . We then argue that the situation is similar to that of high-quality assume guarantee synthesis [ 3 ], where richer relations between a quantitative assumption and a quantitative guarantee are of interest. In our case, the assumption is the hopefulness level of the input sequence, namely , and the guarantee is the satisfaction value of the specification in the generated computation, namely .…”

Section: Introductionmentioning

confidence: 99%

Good-Enough Synthesis

Almagor

Kupferman

2020

Computer Aided Verification

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We introduce and study good-enough synthesis (gesynthesis)-a variant of synthesis in which the system is required to satisfy a given specification ψ only when it interacts with an environments for which a satisfying interaction exists. Formally, an input sequence x is hopeful if there exists some output sequence y such that the induced computation x⊗y satisfies ψ, and a system ge-realizes ψ if it generates a computation that satisfies ψ on all hopeful input sequences. ge-synthesis is particularly relevant when the notion of correctness is multi-valued (rather than Boolean), and thus we seek systems of the highest possible quality, and when synthesizing autonomous systems, which interact with unexpected environments and are often only expected to do their best. We study ge-synthesis in Boolean and multi-valued settings. In both, we suggest and solve various definitions of ge-synthesis, corresponding to different ways a designer may want to take hopefulness into account. We show that in all variants, ge-synthesis is not computationally harder than traditional synthesis, and can be implemented on top of existing tools. Our algorithms are based on careful combinations of nondeterministic and universal automata. We augment systems that ge-realize their specifications by monitors that provide satisfaction information. In the multi-valued setting, we provide both a worst-case analysis and an expectation-based one, the latter corresponding to an interaction with a stochastic environment.

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Quantitative Assume Guarantee Synthesis

Cited by 15 publications

References 29 publications

Environmentally-Friendly GR(1) Synthesis

Environmentally-Friendly GR(1) Synthesis

Reasoning about Quality and Fuzziness of Strategic Behaviours

Good-Enough Synthesis

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