On High-Quality Synthesis

Kupferman, Orna

doi:10.1007/978-3-319-34171-2_1

Cited by 5 publications

(5 citation statements)

References 43 publications

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“…Indeed, rarely is the intention for the behaviour of a system formulated entirely in black and white: We prefer a programm just crashing to accidently erasing our hard-drive; we prefer a programm to complete its task in 1 minute to it taking 5 minutes, etc. We point to [13] for a recent survey on such notions of quality in synthesis.…”

Section: Introductionmentioning

confidence: 99%

Extending Finite Memory Determinacy to Multiplayer Games

Roux

Pauly

2016

Electron. Proc. Theor. Comput. Sci.

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We show that under some general conditions the finite memory determinacy of a class of two-player win/lose games played on finite graphs implies the existence of a Nash equilibrium built from finite memory strategies for the corresponding class of multi-player multi-outcome games. This generalizes a previous result by Brihaye, De Pril and Schewe. For most of our conditions we provide counterexamples showing that they cannot be dispensed with.Our proofs are generally constructive, that is, provide upper bounds for the memory required, as well as algorithms to compute the relevant winning strategies.

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Section: Introductionmentioning

confidence: 99%

Extending Finite Memory Determinacy to Multiplayer Games

Roux

Pauly

2016

Electron. Proc. Theor. Comput. Sci.

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“…A popular research direction, orthogonal and complementary to the one proposed here, is to extend the formal specification with quantitative aspects, see e.g. [5,9,24,2], and only synthesize solutions that are optimal.…”

Section: Example 3 [Synthesis From ϕ Mementioning

confidence: 99%

LTL Reactive Synthesis with a Few Hints

Mrudula¹,

Filiot²,

Raskin³

2023

Preprint

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We study a variant of the problem of synthesizing Mealy machines that enforce LTL specifications against all possible behaviours of the environment including hostile ones. In the variant studied here, the user provides the high level LTL specification ϕ of the system to design, and a set E of examples of executions that the solution must produce. Our synthesis algorithm works in two phases. First, it generalizes the decisions taken along the examples E using tailored extensions of automata learning algorithms. This phase generalizes the user-provided examples in E while preserving realizability of ϕ. Second, the algorithm turns the (usually) incomplete Mealy machine obtained by the learning phase into a complete Mealy machine that realizes ϕ. The examples are used to guide the synthesis procedure. We provide a completness result that shows that our procedure can learn any Mealy machine M that realizes ϕ with a small (polynomial) set of examples. We also show that our problem, that generalizes the classical LTL synthesis problem (i.e. when E = ∅), matches its worst-case complexity. The additional cost of learning from E is even polynomial in the size of E and in the size of a symbolic representation of solutions that realize ϕ. This symbolic representation is computed by the synthesis algorithm implemented in Acacia-Bonzai when solving the plain LTL synthesis problem. We illustrate the practical interest of our approach on a set of examples.

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“…A popular research direction, orthogonal and complementary to the one proposed here, is to extend the formal specification with quantitative aspects, see e.g. [6,10,22,2], and only synthesize solutions that are optimal.…”

Section: Introductionmentioning

confidence: 99%

LTL Reactive Synthesis with a Few Hints

Mrudula

Filiot

Raskin

2023

Lecture Notes in Computer Science

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We study a variant of the problem of synthesizing Mealy machines that enforce LTL specifications against all possible behaviours of the environment, including hostile ones. In the variant studied here, the user provides the high level LTL specification $$\varphi $$ φ of the system to design, and a set E of examples of executions that the solution must produce. Our synthesis algorithm first generalizes the user-provided examples in E using tailored extensions of automata learning algorithms, while preserving realizability of $$\varphi $$ φ . Second, it turns the (usually) incomplete Mealy machine obtained by the learning phase into a complete Mealy machine realizing $$\varphi $$ φ . The examples are used to guide the synthesis procedure. We prove learnability guarantees of our algorithm and prove that our problem, while generalizing the classical LTL synthesis problem, matches its worst-case complexity. The additional cost of learning from E is even polynomial in the size of E and in the size of a symbolic representation of solutions that realize $$\varphi $$ φ , computed by the synthesis tool Acacia-Bonzai. We illustrate the practical interest of our approach on a set of examples.

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On High-Quality Synthesis

Cited by 5 publications

References 43 publications

Extending Finite Memory Determinacy to Multiplayer Games

Extending Finite Memory Determinacy to Multiplayer Games

LTL Reactive Synthesis with a Few Hints

LTL Reactive Synthesis with a Few Hints

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