Pub Date : 2022-07-29DOI: 10.48550/arXiv.2207.14417
Muqsit Azeem, Alexandros Evangelidis, Jan Křetínský, Alexander Slivinskiy, Maximilian Weininger
. While value iteration (VI) is a standard solution approach to simple stochastic games (SSGs), it suffered from the lack of a stopping criterion. Recently, several solutions have appeared, among them also “optimistic” VI (OVI). However, OVI is applicable only to one-player SSGs with no end components. We lift these two assumptions, making it available to general SSGs . Further, we utilize the idea in the context of topological VI, where we provide an efficient precise solution. In order to compare the new algorithms with the state of the art, we use not only the standard benchmarks, but we also design a random generator of SSGs, which can be biased towards various types of models, aiding in understanding the advantages of different algorithms on SSGs.
{"title":"Optimistic and Topological Value Iteration for Simple Stochastic Games","authors":"Muqsit Azeem, Alexandros Evangelidis, Jan Křetínský, Alexander Slivinskiy, Maximilian Weininger","doi":"10.48550/arXiv.2207.14417","DOIUrl":"https://doi.org/10.48550/arXiv.2207.14417","url":null,"abstract":". While value iteration (VI) is a standard solution approach to simple stochastic games (SSGs), it suffered from the lack of a stopping criterion. Recently, several solutions have appeared, among them also “optimistic” VI (OVI). However, OVI is applicable only to one-player SSGs with no end components. We lift these two assumptions, making it available to general SSGs . Further, we utilize the idea in the context of topological VI, where we provide an efficient precise solution. In order to compare the new algorithms with the state of the art, we use not only the standard benchmarks, but we also design a random generator of SSGs, which can be biased towards various types of models, aiding in understanding the advantages of different algorithms on SSGs.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"281 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114258025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-27DOI: 10.48550/arXiv.2207.13416
V. Dave, S. Krishna, Vishnu Murali, Ashutosh Trivedi
This paper presents an optimization based framework to automate system repair against omega-regular properties. In the proposed formalization of optimal repair, the systems are represented as Kripke structures, the properties as $omega$-regular languages, and the repair space as repair machines -- weighted omega-regular transducers equipped with B"uchi conditions -- that rewrite strings and associate a cost sequence to these rewritings. To translate the resulting cost-sequences to easily interpretable payoffs, we consider several aggregator functions to map cost sequences to numbers -- including limit superior, supremum, discounted-sum, and average-sum -- to define quantitative cost semantics. The problem of optimal repair, then, is to determine whether traces from a given system can be rewritten to satisfy an $omega$-regular property when the allowed cost is bounded by a given threshold. We also consider the dual challenge of impair verification that assumes that the rewritings are resolved adversarially under some given cost restriction, and asks to decide if all traces of the system satisfy the specification irrespective of the rewritings. With a negative result to the impair verification problem, we study the problem of designing a minimal mask of the Kripke structure such that the resulting traces satisfy the specifications despite the threshold-bounded impairment. We dub this problem as the mask synthesis problem. This paper presents automata-theoretic solutions to repair synthesis, impair verification, and mask synthesis problem for limit superior, supremum, discounted-sum, and average-sum cost semantics.
{"title":"Optimal Repair For Omega-regular Properties","authors":"V. Dave, S. Krishna, Vishnu Murali, Ashutosh Trivedi","doi":"10.48550/arXiv.2207.13416","DOIUrl":"https://doi.org/10.48550/arXiv.2207.13416","url":null,"abstract":"This paper presents an optimization based framework to automate system repair against omega-regular properties. In the proposed formalization of optimal repair, the systems are represented as Kripke structures, the properties as $omega$-regular languages, and the repair space as repair machines -- weighted omega-regular transducers equipped with B\"uchi conditions -- that rewrite strings and associate a cost sequence to these rewritings. To translate the resulting cost-sequences to easily interpretable payoffs, we consider several aggregator functions to map cost sequences to numbers -- including limit superior, supremum, discounted-sum, and average-sum -- to define quantitative cost semantics. The problem of optimal repair, then, is to determine whether traces from a given system can be rewritten to satisfy an $omega$-regular property when the allowed cost is bounded by a given threshold. We also consider the dual challenge of impair verification that assumes that the rewritings are resolved adversarially under some given cost restriction, and asks to decide if all traces of the system satisfy the specification irrespective of the rewritings. With a negative result to the impair verification problem, we study the problem of designing a minimal mask of the Kripke structure such that the resulting traces satisfy the specifications despite the threshold-bounded impairment. We dub this problem as the mask synthesis problem. This paper presents automata-theoretic solutions to repair synthesis, impair verification, and mask synthesis problem for limit superior, supremum, discounted-sum, and average-sum cost semantics.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129534735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-27DOI: 10.48550/arXiv.2207.13446
Masaki Waga, Ezequiel Castellano, Sasinee Pruekprasert, Stefan Klikovits, Toru Takisaka, I. Hasuo
. It is challenging to use reinforcement learning (RL) in cyber-physical systems due to the lack of safety guarantees during learning. Although there have been various proposals to reduce undesired behaviors during learning, most of these techniques require prior system knowledge, and their applicability is limited. This paper aims to reduce undesired behaviors during learning without requiring any prior system knowledge. We propose dynamic shielding : an extension of a model-based safe RL technique called shielding using data-driven automata learning . The dynamic shielding technique constructs an approximate system model in parallel with RL using a variant of the RPNI algorithm and sup-presses undesired explorations due to the shield constructed from the learned model. Through this combination, potentially unsafe actions can be foreseen before the agent experiences them. Experiments show that our dynamic shield significantly decreases the number of undesired events during training. and experiment results.
{"title":"Dynamic Shielding for Reinforcement Learning in Black-Box Environments","authors":"Masaki Waga, Ezequiel Castellano, Sasinee Pruekprasert, Stefan Klikovits, Toru Takisaka, I. Hasuo","doi":"10.48550/arXiv.2207.13446","DOIUrl":"https://doi.org/10.48550/arXiv.2207.13446","url":null,"abstract":". It is challenging to use reinforcement learning (RL) in cyber-physical systems due to the lack of safety guarantees during learning. Although there have been various proposals to reduce undesired behaviors during learning, most of these techniques require prior system knowledge, and their applicability is limited. This paper aims to reduce undesired behaviors during learning without requiring any prior system knowledge. We propose dynamic shielding : an extension of a model-based safe RL technique called shielding using data-driven automata learning . The dynamic shielding technique constructs an approximate system model in parallel with RL using a variant of the RPNI algorithm and sup-presses undesired explorations due to the shield constructed from the learned model. Through this combination, potentially unsafe actions can be foreseen before the agent experiences them. Experiments show that our dynamic shield significantly decreases the number of undesired events during training. and experiment results.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126919463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-14DOI: 10.48550/arXiv.2206.06722
Simon Lutz, D. Neider, Rajarshi Roy
Virtually all verification and synthesis techniques assume that the formal specifications are readily available, functionally correct, and fully match the engineer's understanding of the given system. However, this assumption is often unrealistic in practice: formalizing system requirements is notoriously difficult, error-prone, and requires substantial training. To alleviate this severe hurdle, we propose a fundamentally novel approach to writing formal specifications, named specification sketching for Linear Temporal Logic (LTL). The key idea is that an engineer can provide a partial LTL formula, called an LTL sketch, where parts that are hard to formalize can be left out. Given a set of examples describing system behaviors that the specification should or should not allow, the task of a so-called sketching algorithm is then to complete a given sketch such that the resulting LTL formula is consistent with the examples. We show that deciding whether a sketch can be completed falls into the complexity class NP and present two SAT-based sketching algorithms. We also demonstrate that sketching is a practical approach to writing formal specifications using a prototype implementation.
{"title":"Specification sketching for Linear Temporal Logic","authors":"Simon Lutz, D. Neider, Rajarshi Roy","doi":"10.48550/arXiv.2206.06722","DOIUrl":"https://doi.org/10.48550/arXiv.2206.06722","url":null,"abstract":"Virtually all verification and synthesis techniques assume that the formal specifications are readily available, functionally correct, and fully match the engineer's understanding of the given system. However, this assumption is often unrealistic in practice: formalizing system requirements is notoriously difficult, error-prone, and requires substantial training. To alleviate this severe hurdle, we propose a fundamentally novel approach to writing formal specifications, named specification sketching for Linear Temporal Logic (LTL). The key idea is that an engineer can provide a partial LTL formula, called an LTL sketch, where parts that are hard to formalize can be left out. Given a set of examples describing system behaviors that the specification should or should not allow, the task of a so-called sketching algorithm is then to complete a given sketch such that the resulting LTL formula is consistent with the examples. We show that deciding whether a sketch can be completed falls into the complexity class NP and present two SAT-based sketching algorithms. We also demonstrate that sketching is a practical approach to writing formal specifications using a prototype implementation.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123812013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-16DOI: 10.48550/arXiv.2205.07736
Chih-Hong Cheng, Changshun Wu, Emmanouil Seferis, S. Bensalem
. For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training dataset. This paper stud-ies the problem of systematically testing OoD monitors to avoid cases where an input data point is tested as in-distribution by the monitor, but the DNN produces spurious output predictions. We consider the def-inition of “in-distribution” characterized in the feature space by a union of hyperrectangles learned from the training dataset. Thus the testing is reduced to finding corners in hyperrectangles distant from the available training data in the feature space. Concretely, we encode the abstract lo-cation of every data point as a finite-length binary string, and the union of all binary strings is stored compactly using binary decision diagrams (BDDs). We demonstrate how to use BDDs to symbolically extract corners distant from all data points within the training set. Apart from test case generation, we explain how to use the proposed corners to fine-tune the DNN to ensure that it does not predict overly confidently. The result is evaluated over examples such as number and traffic sign recognition.
{"title":"Prioritizing Corners in OoD Detectors via Symbolic String Manipulation","authors":"Chih-Hong Cheng, Changshun Wu, Emmanouil Seferis, S. Bensalem","doi":"10.48550/arXiv.2205.07736","DOIUrl":"https://doi.org/10.48550/arXiv.2205.07736","url":null,"abstract":". For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training dataset. This paper stud-ies the problem of systematically testing OoD monitors to avoid cases where an input data point is tested as in-distribution by the monitor, but the DNN produces spurious output predictions. We consider the def-inition of “in-distribution” characterized in the feature space by a union of hyperrectangles learned from the training dataset. Thus the testing is reduced to finding corners in hyperrectangles distant from the available training data in the feature space. Concretely, we encode the abstract lo-cation of every data point as a finite-length binary string, and the union of all binary strings is stored compactly using binary decision diagrams (BDDs). We demonstrate how to use BDDs to symbolically extract corners distant from all data points within the training set. Apart from test case generation, we explain how to use the proposed corners to fine-tune the DNN to ensure that it does not predict overly confidently. The result is evaluated over examples such as number and traffic sign recognition.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130670969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-12DOI: 10.48550/arXiv.2205.06039
B. Finkbeiner, Jana Hofmann, F. Kohn, Noemi E. Passing
Smart contracts are small but highly error-prone programs that implement agreements between multiple parties. We present a reactive synthesis approach for the automatic construction of smart contract state machines. Towards this end, we extend temporal stream logic (TSL) with universally quantified parameters over infinite domains. Parameterized TSL is a convenient logic to specify the temporal control flow, i.e., the correct order of transactions, as well as the data flow of the contract's fields. We develop a two-step approach that 1) synthesizes a finite representation of the - in general - infinite-state system and 2) splits the system into a compact hierarchical architecture that enables the implementation of the state machine in Solidity. We implement the approach in our prototype tool SCSynt, which - within seconds - automatically constructs Solidity code that realizes the specified control flow.
{"title":"Reactive Synthesis of Smart Contract Control Flows","authors":"B. Finkbeiner, Jana Hofmann, F. Kohn, Noemi E. Passing","doi":"10.48550/arXiv.2205.06039","DOIUrl":"https://doi.org/10.48550/arXiv.2205.06039","url":null,"abstract":"Smart contracts are small but highly error-prone programs that implement agreements between multiple parties. We present a reactive synthesis approach for the automatic construction of smart contract state machines. Towards this end, we extend temporal stream logic (TSL) with universally quantified parameters over infinite domains. Parameterized TSL is a convenient logic to specify the temporal control flow, i.e., the correct order of transactions, as well as the data flow of the contract's fields. We develop a two-step approach that 1) synthesizes a finite representation of the - in general - infinite-state system and 2) splits the system into a compact hierarchical architecture that enables the implementation of the state machine in Solidity. We implement the approach in our prototype tool SCSynt, which - within seconds - automatically constructs Solidity code that realizes the specified control flow.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126214991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-08DOI: 10.1007/978-3-030-88885-5_19
Norine Coenen, B. Finkbeiner, Christopher Hahn, Jana Hofmann, Yannick Schillo
{"title":"Runtime Enforcement of Hyperproperties","authors":"Norine Coenen, B. Finkbeiner, Christopher Hahn, Jana Hofmann, Yannick Schillo","doi":"10.1007/978-3-030-88885-5_19","DOIUrl":"https://doi.org/10.1007/978-3-030-88885-5_19","url":null,"abstract":"","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129890527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.1007/978-3-031-19992-9_11
Jiong Yang, Supratik Chakraborty, Kuldeep S. Meel
{"title":"Projected Model Counting: Beyond Independent Support","authors":"Jiong Yang, Supratik Chakraborty, Kuldeep S. Meel","doi":"10.1007/978-3-031-19992-9_11","DOIUrl":"https://doi.org/10.1007/978-3-031-19992-9_11","url":null,"abstract":"","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126181675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-05DOI: 10.1007/978-3-030-88885-5_8
Brae J. Webb, M. Utting, I. Hayes
{"title":"A Formal Semantics of the GraalVM Intermediate Representation","authors":"Brae J. Webb, M. Utting, I. Hayes","doi":"10.1007/978-3-030-88885-5_8","DOIUrl":"https://doi.org/10.1007/978-3-030-88885-5_8","url":null,"abstract":"","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130516761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-02DOI: 10.1007/978-3-030-88885-5_22
S. Azzopardi, Nir Piterman, G. Schneider
{"title":"Incorporating Monitors in Reactive Synthesis without Paying the Price","authors":"S. Azzopardi, Nir Piterman, G. Schneider","doi":"10.1007/978-3-030-88885-5_22","DOIUrl":"https://doi.org/10.1007/978-3-030-88885-5_22","url":null,"abstract":"","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131752543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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