Improving the Model Checking of Strategies under Partial Observability and Fairness Constraints

Busard, Simon; Pecheur, Charles; Qu, Hongyang; Raimondi, Franco

doi:10.1007/978-3-319-11737-9_3

Cited by 13 publications

(32 citation statements)

References 11 publications

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“…Some early attempts at verification of imperfect information strategies made their way into the MCMAS model-checker [25,28,23,24], but the issue was never at the heart of the tool. More dedicated attempts began to emerge only recently [26,8,17,9]. Up until now, experimental results confirm that the initial intuition was right: model checking of strategic modalities for imperfect information is hard, and dealing with it requires innovative algorithms and verification techniques.…”

Section: Introductionmentioning

confidence: 81%

“…We note, however, that imperfect information strategies are not really at the heart of the model-checker, the focus being on verification of CTLK and ATLK with perfect information strategies. More dedicated attempts produced so far only experimental algorithms, with preliminary performance results reported in [26,8,17,9,27]. Because of that, there are few benchmarks for model checking ATLir, and few experiments have actually been conducted.…”

Section: Existing Benchmarksmentioning

confidence: 99%

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Approximate verification of strategic abilities under imperfect information

Jamroga

Knapik

Kurpiewski

et al. 2019

Artificial Intelligence

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Model checking of strategic ability under imperfect information is known to be hard. The complexity results range from NP-completeness to undecidability, depending on the precise setup of the problem. No less importantly, fixpoint equivalences do not generally hold for imperfect information strategies, which seriously hampers incremental synthesis of winning strategies.In this paper, we propose translations of ATLir formulae that provide lower and upper bounds for their truth values, and are cheaper to verify than the original specifications. That is, if the expression is verified as true then the corresponding formula of ATLir should also hold in the given model. We begin by showing where the straightforward approach does not work. Then, we propose how it can be modified to obtain guaranteed lower bounds. To this end, we alter the next-step operator in such a way that traversing one's indistinguishability relation is seen as atomic activity. Most interestingly, the lower approximation is provided by a fixpoint expression that uses a nonstandard variant of the next-step ability operator. We show the correctness of the translations, establish their computational complexity, and validate the approach by experiments with a scalable scenario of Bridge play.

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Section: Introductionmentioning

confidence: 81%

Section: Existing Benchmarksmentioning

confidence: 99%

Approximate verification of strategic abilities under imperfect information

Jamroga

Knapik

Kurpiewski

et al. 2019

Artificial Intelligence

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show abstract

“…First, the work presented in this paper has been extended by the authors [21]. The idea of this extension is to improve the model-checking process by reducing the number of strategies to check.…”

Section: Related Workmentioning

confidence: 96%

Reasoning about memoryless strategies under partial observability and unconditional fairness constraints

Busard

Pecheur

et al. 2015

Information and Computation

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Alternating-time Temporal Logic is a logic to reason about strategies that agents can adopt to achieve a specified collective goal. A number of extensions for this logic exist; some of them combine strategies and partial observability, some others include fairness constraints, but to the best of our knowledge no work provides a unified framework for strategies, partial observability and fairness constraints. Integration of these three concepts is important when reasoning about the capabilities of agents without full knowledge of a system, for instance when the agents can assume that the environment behaves in a fair way. We present ATLK irF , a logic combining strategies under partial observability in a system with fairness constraints on states. We introduce a model-checking algorithm for ATLK irF by extending the algorithm for a full-observability variant of the logic and we investigate its complexity. We validate our proposal with an experimental evaluation.

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“…Some extensions of this logic remove this assumption and reason about the strategies of agents that have only a partial view of the system (see for instance [24,21,16], and a comparison of strategic abilities in [4]). AT LK irF is a logic that reasons about the strategies of memoryless agents with imperfect information-we speak about uniform strategies-in a system with unconditional fairness constraints [8].…”

Section: Introductionmentioning

confidence: 99%