Robustness-by-Construction Synthesis: Adapting to the Environment at Runtime

While most of the current synthesis algorithms only focus on correctness-by-construction, ensuring robustness has remained a challenge. Hence, in this paper, we address the robust-by-construction synthesis problem by considering the specifications to be expressed by a robust version of Linear Temporal Logic (LTL), called robust LTL (rLTL). rLTL has a many-valued semantics to capture different degrees of satisfaction of a specification, i.e., satisfaction is a quantitative notion. We argue that the current algorithms for rLTL synthesis do not compute optimal strategies in a non-antagonistic setting. So, a natural question is whether there is a way of satisfying the specification "better" if the environment is indeed not antagonistic. We address this question by developing two new notions of strategies. The first notion is that of adaptive strategies, which, in response to the opponent's non-antagonistic moves, maximize the degree of satisfaction. The idea is to monitor non-optimal moves of the opponent at runtime using multiple parity automata and adaptively change the system strategy to ensure optimality. The second notion is that of strongly adaptive strategies, which is a further refinement of the first notion. These strategies also maximize the opportunities for the opponent to make non-optimal moves. We show that computing such strategies for rLTL specifications is not harder than the standard synthesis problem, e.g., computing strategies with LTL specifications, and takes doubly-exponential time.Comment: 32 pages, 3 figure

Synthesizing Permissive Winning Strategy Templates for Parity Games

We present a novel method to compute \emph{permissive winning strategies} in two-player games over finite graphs with $\omega$-regular winning conditions. Given a game graph $G$ and a parity winning condition $\Phi$, we compute a \emph{winning strategy template} $\Psi$ that collects an infinite number of winning strategies for objective $\Phi$ in a concise data structure. We use this new representation of sets of winning strategies to tackle two problems arising from applications of two-player games in the context of cyber-physical system design -- (i) \emph{incremental synthesis}, i.e., adapting strategies to newly arriving, \emph{additional} $\omega$-regular objectives $\Phi'$, and (ii) \emph{fault-tolerant control}, i.e., adapting strategies to the occasional or persistent unavailability of actuators. The main features of our strategy templates -- which we utilize for solving these challenges -- are their easy computability, adaptability, and compositionality. For \emph{incremental synthesis}, we empirically show on a large set of benchmarks that our technique vastly outperforms existing approaches if the number of added specifications increases. While our method is not complete, our prototype implementation returns the full winning region in all 1400 benchmark instances, i.e., handling a large problem class efficiently in practice.Comment: CAV'2

Computing Adequately Permissive Assumptions for Synthesis

We solve the problem of automatically computing a new class of environment assumptions in two-player turn-based finite graph games which characterize an ``adequate cooperation'' needed from the environment to allow the system player to win. Given an $\omega$-regular winning condition $\Phi$ for the system player, we compute an $\omega$-regular assumption $\Psi$ for the environment player, such that (i) every environment strategy compliant with $\Psi$ allows the system to fulfill $\Phi$ (sufficiency), (ii) $\Psi$ can be fulfilled by the environment for every strategy of the system (implementability), and (iii) $\Psi$ does not prevent any cooperative strategy choice (permissiveness). For parity games, which are canonical representations of $\omega$-regular games, we present a polynomial-time algorithm for the symbolic computation of adequately permissive assumptions and show that our algorithm runs faster and produces better assumptions than existing approaches -- both theoretically and empirically. To the best of our knowledge, for $\omega$-regular games, we provide the first algorithm to compute sufficient and implementable environment assumptions that are also permissive.Comment: TACAS 202

Context-triggered Abstraction-based Control Design

We consider the problem of automatically synthesizing a hybrid controller for non-linear dynamical systems which ensures that the closed-loop fulfills an arbitrary \emph{Linear Temporal Logic} specification. Moreover, the specification may take into account logical context switches induced by an external environment or the system itself. Finally, we want to avoid classical brute-force time- and space-discretization for scalability. We achieve these goals by a novel two-layer strategy synthesis approach, where the controller generated in the lower layer provides invariant sets and basins of attraction, which are exploited at the upper logical layer in an abstract way. In order to achieve this, we provide new techniques for both the upper- and lower-level synthesis. Our new methodology allows to leverage both the computing power of state space control techniques and the intelligence of finite game solving for complex specifications, in a scalable way

Globulin seed storage protein based genotyping and Study of genetic diversity in core accessions of mungbean under drought stress

Globulin seed storage protein profiles of 19 mungbean genotypes including two wild forms of Vigna radiata var. sublobata(TCR 20 and TCR 213) and two standard  checks(T 2-1 and LGG 460) were analysed by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). Thirteen genotypes could be clearly identified based on genotype-specific seed protein fingerprints. The combined dendrogram showed six genetic clusters within 68% phenon level. The clustering based on the combined clustering analysis revealed discrimination of all test genotypes even immediately beyond 88% phenon level, whereas individual clustering analysis based on protein and agro-morphological level failed to do so. Nipania munga, TCR 213, T 2-1, LGG 460, TCR 20 and Banapur local B were identified to be highly divergent genotypes. TCR 20 appears to have more genetic proximity to the mungbean genotypes than TCR 213. T 2-1, LGG 460 and TCR 20 are potentially high yielding. These may serve as valuable materials for recombination breeding in mungbean

Robustness-by-Construction Synthesis: Adapting to the Environment at Runtime

While most of the current synthesis algorithms only focus on correctness-by-construction, ensuring robustness has remained a challenge. Hence, in this paper, we address the robust-by-construction synthesis problem by considering the specifications to be expressed by a robust version of Linear Temporal Logic (LTL ), called robust LTL (rLTL ). rLTL has a many-valued semantics to capture different degrees of satisfaction of a specification, i.e., satisfaction is a quantitative notion. We argue that the current algorithms for rLTL synthesis do not compute optimal strategies in a non-antagonistic setting. So, a natural question is whether there is a way of satisfying the specification “better” if the environment is indeed not antagonistic. We address this question by developing two new notions of strategies. The first notion is that of adaptive strategies, which, in response to the opponent’s non-antagonistic moves, maximize the degree of satisfaction. The idea is to monitor non-optimal moves of the opponent at runtime using multiple parity automata and adaptively change the system strategy to ensure optimality. The second notion is that of strongly adaptive strategies, which is a further refinement of the first notion. These strategies also maximize the opportunities for the opponent to make non-optimal moves. We show that computing such strategies for rLTL specifications is not harder than the standard synthesis problem, e.g., computing strategies with LTL specifications, and takes doubly-exponential time

Robust Computation Tree Logic

It is widely accepted that every system should be robust in that "small" violations of environment assumptions should lead to "small" violations of system guarantees, but it is less clear how to make this intuition mathematically precise. While significant efforts have been devoted to providing notions of robustness for Linear Temporal Logic (LTL), branching-time logics, such as Computation Tree Logic (CTL) and CTL*, have received less attention in this regard. To address this shortcoming, we develop "robust" extensions of CTL and CTL*, which we name robust CTL (rCTL) and robust CTL* (rCTL*). Both extensions are syntactically similar to their parent logics but employ multi-valued semantics to distinguish between "large" and "small" violations of the specification. We show that the multi-valued semantics of rCTL make it more expressive than CTL, while rCTL* is as expressive as CTL*. Moreover, we devise efficient model checking algorithms for rCTL and rCTL*, which have the same asymptotic time complexity as the model checking algorithms for CTL and CTL*, respectively.Comment: 23 pages, 1 figure, to be published in the proceedings of NASA Formal Methods (NFM), 202

LNCS

We automatically compute a new class of environment assumptions in two-player turn-based finite graph games which characterize an “adequate cooperation” needed from the environment to allow the system player to win. Given an ω-regular winning condition Φ for the system player, we compute an ω-regular assumption Ψ for the environment player, such that (i) every environment strategy compliant with Ψ allows the system to fulfill Φ (sufficiency), (ii) Ψ can be fulfilled by the environment for every strategy of the system (implementability), and (iii) Ψ does not prevent any cooperative strategy choice (permissiveness). For parity games, which are canonical representations of ω-regular games, we present a polynomial-time algorithm for the symbolic computation of adequately permissive assumptions and show that our algorithm runs faster and produces better assumptions than existing approaches—both theoretically and empirically. To the best of our knowledge, for ω -regular games, we provide the first algorithm to compute sufficient and implementable environment assumptions that are also permissive

Docker Image for HSCC'24 paper 53

This is a docker image that can be used to regenerate results of the case study reported in HSCC'24 paper 53.</p