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b929b639c7314b24f3dd0b1d9cb02180c8217d7f Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Tue, 9 Jan 2024 15:41:50 +0000 Subject: [PATCH 2/3] Creating 10.21105.joss.05052.crossref.xml --- joss.05052/10.21105.joss.05052.crossref.xml | 325 ++++++++++++++++++++ 1 file changed, 325 insertions(+) create mode 100644 joss.05052/10.21105.joss.05052.crossref.xml diff --git a/joss.05052/10.21105.joss.05052.crossref.xml b/joss.05052/10.21105.joss.05052.crossref.xml new file mode 100644 index 0000000000..06201d9c9b --- /dev/null +++ b/joss.05052/10.21105.joss.05052.crossref.xml @@ -0,0 +1,325 @@ + + + + 20240109T154115-a7950d0954479c95ff29003eb332e0679d303835 + 20240109154115 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 01 + 2024 + + + 9 + + 93 + + + + Scarlet: Scalable Anytime Algorithms for Learning +Fragments of Linear Temporal Logic + + + + Ritam + Raha + https://orcid.org/0000-0003-1467-1182 + + + Rajarshi + Roy + https://orcid.org/0000-0002-0202-1169 + + + Nathanaël + Fijalkow + https://orcid.org/0000-0002-6576-4680 + + + Daniel + Neider + https://orcid.org/0000-0001-9276-6342 + + + + 01 + 09 + 2024 + + + 5052 + + + 10.21105/joss.05052 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10419514 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5052 + + + + 10.21105/joss.05052 + https://joss.theoj.org/papers/10.21105/joss.05052 + + + https://joss.theoj.org/papers/10.21105/joss.05052.pdf + + + + + + Specification mining: New formalisms, +algorithms and applications + Li + 2013 + Li, W. (2013). Specification mining: +New formalisms, algorithms and applications [PhD thesis, University of +California, Berkeley, USA]. +http://www.escholarship.org/uc/item/4027r49r + + + Formal requirements elicitation with +FRET + Giannakopoulou + International conference on requirements +engineering: Foundation for software quality, REFSQ + 2020 + Giannakopoulou, D., Pressburger, T., +Mavridou, A., Rhein, J., Schumann, J., & Shi, N. (2020). Formal +requirements elicitation with FRET. International Conference on +Requirements Engineering: Foundation for Software Quality, REFSQ. +http://ceur-ws.org/Vol-2584/PT-paper4.pdf + + + ARSENAL: Automatic requirements specification +extraction from natural language + Ghosh + NASA formal methods, NFM + 10.1007/978-3-319-40648-0_4 + 2016 + Ghosh, S., Elenius, D., Li, W., +Lincoln, P., Shankar, N., & Steiner, W. (2016). ARSENAL: Automatic +requirements specification extraction from natural language. NASA Formal +Methods, NFM. +https://doi.org/10.1007/978-3-319-40648-0_4 + + + The complexity of learning linear temporal +formulas from examples + Fijalkow + International conference on grammatical +inference, ICGI + 2021 + Fijalkow, N., & Lagarde, G. +(2021). The complexity of learning linear temporal formulas from +examples. International Conference on Grammatical Inference, ICGI. +https://proceedings.mlr.press/v153/fijalkow21a.html + + + Learning linear temporal +properties + Neider + Formal methods in computer aided design, +FMCAD + 10.23919/FMCAD.2018.8603016 + 2018 + Neider, D., & Gavran, I. (2018). +Learning linear temporal properties. Formal Methods in Computer Aided +Design, FMCAD. +https://doi.org/10.23919/FMCAD.2018.8603016 + + + Explaining multi-stage tasks by learning +temporal logic formulas from suboptimal demonstrations + Chou + Robotics: Science and systems + 10.15607/RSS.2020.XVI.097 + 2020 + Chou, G., Ozay, N., & Berenson, +D. (2020). Explaining multi-stage tasks by learning temporal logic +formulas from suboptimal demonstrations. Robotics: Science and Systems. +https://doi.org/10.15607/RSS.2020.XVI.097 + + + Learning interpretable models in the property +specification language + Roy + International joint conference on artificial +intelligence, IJCAI + 10.24963/ijcai.2020/306 + 2020 + Roy, R., Fisman, D., & Neider, D. +(2020). Learning interpretable models in the property specification +language. International Joint Conference on Artificial Intelligence, +IJCAI, 2213–2219. +https://doi.org/10.24963/ijcai.2020/306 + + + Learning properties in LTL \cap ACTL from +positive examples only + Ehlers + Formal methods in computer aided design, +FMCAD + 10.34727/2020/isbn.978-3-85448-042-6_17 + 2020 + Ehlers, R., Gavran, I., & Neider, +D. (2020). Learning properties in LTL \cap ACTL from positive examples +only. Formal Methods in Computer Aided Design, FMCAD, 104–112. +https://doi.org/10.34727/2020/isbn.978-3-85448-042-6_17 + + + Bayesian inference of linear temporal logic +specifications for contrastive explanations + Kim + International joint conference on artificial +intelligence, IJCAI + 10.24963/ijcai.2019/776 + 2019 + Kim, J., Muise, C., Shah, A., +Agarwal, S., & Shah, J. (2019). Bayesian inference of linear +temporal logic specifications for contrastive explanations. +International Joint Conference on Artificial Intelligence, IJCAI. +https://doi.org/10.24963/ijcai.2019/776 + + + Learning interpretable models expressed in +linear temporal logic + Camacho + International Conference on Automated +Planning and Scheduling, ICAPS + 10.1609/icaps.v29i1.3529 + 2019 + Camacho, A., & McIlraith, S. A. +(2019). Learning interpretable models expressed in linear temporal +logic. International Conference on Automated Planning and Scheduling, +ICAPS. https://doi.org/10.1609/icaps.v29i1.3529 + + + General LTL specification +mining + Lemieux + International conference on automated +software engineering, ASE + 10.1109/ASE.2015.71 + 2015 + Lemieux, C., Park, D., & +Beschastnikh, I. (2015). General LTL specification mining. International +Conference on Automated Software Engineering, ASE. +https://doi.org/10.1109/ASE.2015.71 + + + A Decision Tree Approach to Data +Classification using Signal Temporal Logic + Bombara + 10.1145/2883817.2883843 + 2016 + Bombara, G., Vasile, C. I., Penedo +Alvarez, F., Yasuoka, H., & Belta, C. (2016). A Decision Tree +Approach to Data Classification using Signal Temporal Logic. +https://doi.org/10.1145/2883817.2883843 + + + SYSLITE: Syntax-guided synthesis of PLTL +formulas from finite traces + Arif + Formal methods in computer aided design, +FMCAD + 10.34727/2020/ISBN.978-3-85448-042-6_16 + 2020 + Arif, M. F., Larraz, D., Echeverria, +M., Reynolds, A., Chowdhury, O., & Tinelli, C. (2020). SYSLITE: +Syntax-guided synthesis of PLTL formulas from finite traces. Formal +Methods in Computer Aided Design, FMCAD. +https://doi.org/10.34727/2020/ISBN.978-3-85448-042-6_16 + + + cvc4sy: Smart and fast term enumeration for +syntax-guided synthesis + Reynolds + Computer-aided verification, +CAV + 10.1007/978-3-030-25543-5_5 + 2019 + Reynolds, A., Barbosa, H., Nötzli, +A., Barrett, C. W., & Tinelli, C. (2019). cvc4sy: Smart and fast +term enumeration for syntax-guided synthesis. Computer-Aided +Verification, CAV. +https://doi.org/10.1007/978-3-030-25543-5_5 + + + Scalable anytime algorithms for learning +fragments of linear temporal logic + Raha + TACAS (1) + 13243 + 10.1007/978-3-030-99524-9_14 + 2022 + Raha, R., Roy, R., Fijalkow, N., +& Neider, D. (2022). Scalable anytime algorithms for learning +fragments of linear temporal logic. TACAS (1), 13243, 263–280. +https://doi.org/10.1007/978-3-030-99524-9_14 + + + Inferring properties in computation tree +logic + Roy + CoRR + abs/2310.13778 + 10.48550/ARXIV.2310.13778 + 2023 + Roy, R., & Neider, D. (2023). +Inferring properties in computation tree logic. CoRR, abs/2310.13778. +https://doi.org/10.48550/ARXIV.2310.13778 + + + Synthesizing efficiently monitorable formulas +in metric temporal logic + Raha + CoRR + abs/2310.17410 + 10.48550/ARXIV.2310.17410 + 2023 + Raha, R., Roy, R., Fijalkow, N., +Neider, D., & Pérez, G. A. (2023). Synthesizing efficiently +monitorable formulas in metric temporal logic. CoRR, abs/2310.17410. +https://doi.org/10.48550/ARXIV.2310.17410 + + + + + + From 82b9ba37a718e5eb77a67d1eb157067feee269ed Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Tue, 9 Jan 2024 15:41:50 +0000 Subject: [PATCH 3/3] Creating 10.21105.joss.05052.jats --- joss.05052/10.21105.joss.05052.jats | 563 ++++++++++++++++++++++++++++ 1 file changed, 563 insertions(+) create mode 100644 joss.05052/10.21105.joss.05052.jats diff --git a/joss.05052/10.21105.joss.05052.jats b/joss.05052/10.21105.joss.05052.jats new file mode 100644 index 0000000000..8e36f0ea1d --- /dev/null +++ b/joss.05052/10.21105.joss.05052.jats @@ -0,0 +1,563 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5052 +10.21105/joss.05052 + +Scarlet: Scalable Anytime Algorithms for Learning +Fragments of Linear Temporal Logic + + + +https://orcid.org/0000-0003-1467-1182 + +Raha +Ritam + + + +* + + +https://orcid.org/0000-0002-0202-1169 + +Roy +Rajarshi + + +* + + +https://orcid.org/0000-0002-6576-4680 + +Fijalkow +Nathanaël + + + + +https://orcid.org/0000-0001-9276-6342 + +Neider +Daniel + + + + + + +University of Antwerp, Antwerp, Belgium + + + + +CNRS, LaBRI and Université de Bordeaux, +France + + + + +Max Planck Institute for Software Systems, Kaiserslautern, +Germany + + + + +TU Dortmund University, Dortmund, Germany + + + + +Center for Trustworthy Data Science and Security, +University Alliance Ruhr, Germany + + + + +* E-mail: +* E-mail: + + +26 +7 +2022 + +9 +93 +5052 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +linear temporal logic (LTL) +Explainable AI (XAI) +specification mining +Formal Methods + + + + + + Summary +

In the past decade, incorporating data-driven AI techniques in + system design has become mainstream in almost all branches of science + and technology. Typically, systems powered by AI tend to be rather + complex, far beyond human understanding. Naturally, one cannot always + develop trust in such complex, so-called black-box systems, + restricting their widespread use in safety-critical domains.

+

To generate trust in systems, a standard approach in Explainable AI + is to build simple explanations using human-understandable models. A + number of recent works + (Neider + & Gavran, 2018) + (Camacho + & McIlraith, 2019) + (Roy + et al., 2020) have identified models in temporal logic to be + both formal and explainable. Among temporal logics—Linear Temporal + Logic (LTL)—arguably the most widely used temporal logic, has received + particular focus due to its resemblance to natural language. Moreover, + LTL is a de-facto standard in several fields of computer science, + including model-checking, program analysis, and motion planning for + robotics.

+

Scarlet is one of the most competitive tools + for learning explainable models in LTL + (Raha + et al., 2022). To learn such models, it relies on positive (or + desirable) and negative (or undesirable) executions of the system + under consideration. Based on the executions, it learns a concise + model in LTL that is consistent with the given executions.

+

Let us consider a concrete example to understand + Scarlet’s functioning. Consider a robot that + has been designed to collect wastebin contents in an office-like + environment. For the sake of the example, in this environment, let + there be an office + + o + with a wastebin, a hallway + + h, + and a container + + c + to accumulate the waste. The following could be the possible + executions of the robot:

+

+ + h,h,h,h,o,h,c,h + + + h,h,h,h,h,c,h,o,h,h

+

Let the first execution be positive since the robot first collects + waste from the office and then accumulates in the container. Further, + let the second execution be negative since the robot tries to + accumulate the waste in the container even before it collects from the + office. From these executions, Scarlet learns a + model F(o and FX c), where the + F-operator stands for “finally” and + X-operator stands for “next”. This model, in + simple words, expresses that: eventually, the robot should visit the + office + + o + and then, at a later point, should visit the container + + + c.

+
+ + Statement of need +

The fundamental problem solved by Scarlet is + to build an explainable model in the form of an LTL formula from + system executions, formally termed as traces.

+

Scarlet is a tool built entirely in Python + 3. It can be run using its command-line features or its Python API + hosted in PyPi. Its main capabilities include:

+ + +

construction of an LTL formula from execution traces,

+
+ +

generation of execution traces from an LTL formula for testing + LTL learning algorithms (using automata-based techniques, such as + LTLf2DFA and MONA, and random sampling).

+
+
+

Scarlet additionally supports noisy data: + the user can specify a noise threshold (between zero and one, zero for + perfect classification) and the algorithm returns an almost separating + formula with respect to that threshold.

+
+ + Key insights +

A paper presenting the algorithms behind + Scarlet was published in TACAS’2022: Tools and + Algorithms for the Construction and Analysis of Systems + (Raha + et al., 2022).

+

We believe that the path to scalability for learning models in LTL + is to leverage normal forms for LTL formulas and derive efficient + enumeration algorithms from them. Scarlet + combines two insights:

+ + +

An efficient enumeration algorithm for directed LTL formulas, + which are formulas that can be evaluated only moving forward in + traces,

+
+ +

An algorithm solving the Boolean set cover problem, which + constructs Boolean combinations of already constructed formulas in + order to separate positive and negative traces.

+
+
+

For experimental results, refer to the full paper + (Raha + et al., 2022).

+
+ + Related works +

For learning models in LTL, several approaches have been proposed, + leveraging SAT solvers + (Neider + & Gavran, 2018), automata + (Camacho + & McIlraith, 2019), and Bayesian inference + (Kim + et al., 2019). In fact, there are approaches for many temporal + logics such as Property Specification Language (PSL) + (Roy + et al., 2020), Computational Tree Logic (CTL) + (Ehlers + et al., 2020) + (Roy + & Neider, 2023), Metric Temporal Logic (MTL) + (Raha + et al., 2023), etc.

+

Applications of LTL learning include program specification + (Lemieux + et al., 2015), anomaly and fault detection + (Bombara + et al., 2016), robotics + (Chou + et al., 2020), and many more: we refer to + (Camacho + & McIlraith, 2019) for a list of practical applications. An + equivalent point of view on LTL learning is as a specification mining + question. The ARSENAL + (Ghosh + et al., 2016) and FRET + (Giannakopoulou + et al., 2020) projects construct LTL specifications from + natural language; see + (Li, 2013) + for an overview.

+

The two state-of-the-art tools for learning logic formulas from + examples are:

+ + +

FLIE + (Neider + & Gavran, 2018) infers minimal LTL formulas using a + learning algorithm that is based on constraint solving (SAT + solving).

+
+ +

SYSLITE + (Arif + et al., 2020) infers minimal past-time LTL formulas using + an enumerative algorithm implemented in a tool called CVC4SY + (Reynolds + et al., 2019).

+
+
+

Existing methods do not scale beyond formulas of small size, making + them hard to deploy for industrial cases. A second serious limitation + is that they often exhaust computational resources without returning + any results. Indeed theoretical studies + (Fijalkow + & Lagarde, 2021) have shown that constructing the minimal + LTL formula is NP-hard already for very small fragments of LTL, + explaining the difficulties found in practice.

+
+ + Acknowledgments +

This project was funded by the FWO G030020N project + SAILor and Deutsche Forschungsgemeinschaft + (DFG) grant no. 434592664.

+
+ + + + + + + LiWenchao + + Specification mining: New formalisms, algorithms and applications + University of California, Berkeley, USA + 2013 + http://www.escholarship.org/uc/item/4027r49r + + + + + + GiannakopoulouDimitra + PressburgerThomas + MavridouAnastasia + RheinJulian + SchumannJohann + ShiNija + + Formal requirements elicitation with FRET + International conference on requirements engineering: Foundation for software quality, REFSQ + 2020 + http://ceur-ws.org/Vol-2584/PT-paper4.pdf + + + + + + GhoshShalini + EleniusDaniel + LiWenchao + LincolnPatrick + ShankarNatarajan + SteinerWilfried + + ARSENAL: Automatic requirements specification extraction from natural language + NASA formal methods, NFM + 2016 + 10.1007/978-3-319-40648-0_4 + + + + + + FijalkowNathanaël + LagardeGuillaume + + The complexity of learning linear temporal formulas from examples + International conference on grammatical inference, ICGI + 2021 + https://proceedings.mlr.press/v153/fijalkow21a.html + + + + + + NeiderDaniel + GavranIvan + + Learning linear temporal properties + Formal methods in computer aided design, FMCAD + 2018 + 10.23919/FMCAD.2018.8603016 + + + + + + ChouGlen + OzayNecmiye + BerensonDmitry + + Explaining multi-stage tasks by learning temporal logic formulas from suboptimal demonstrations + Robotics: Science and systems + 2020 + 10.15607/RSS.2020.XVI.097 + + + + + + RoyRajarshi + FismanDana + NeiderDaniel + + Learning interpretable models in the property specification language + International joint conference on artificial intelligence, IJCAI + 2020 + 10.24963/ijcai.2020/306 + 2213 + 2219 + + + + + + EhlersRüdiger + GavranIvan + NeiderDaniel + + Learning properties in LTL \cap ACTL from positive examples only + Formal methods in computer aided design, FMCAD + 2020 + 10.34727/2020/isbn.978-3-85448-042-6_17 + 104 + 112 + + + + + + KimJoseph + MuiseChristian + ShahAnkit + AgarwalShubham + ShahJulie + + Bayesian inference of linear temporal logic specifications for contrastive explanations + International joint conference on artificial intelligence, IJCAI + 2019 + 10.24963/ijcai.2019/776 + + + + + + CamachoAlberto + McIlraithSheila A. + + Learning interpretable models expressed in linear temporal logic + International Conference on Automated Planning and Scheduling, ICAPS + 2019 + https://ojs.aaai.org/index.php/ICAPS/article/view/3529 + 10.1609/icaps.v29i1.3529 + + + + + + LemieuxCaroline + ParkDennis + BeschastnikhIvan + + General LTL specification mining + International conference on automated software engineering, ASE + 2015 + 10.1109/ASE.2015.71 + + + + + + BombaraGiuseppe + VasileCristian Ioan + Penedo AlvarezFrancisco + YasuokaHirotoshi + BeltaCalin + + A Decision Tree Approach to Data Classification using Signal Temporal Logic + 2016 + 10.1145/2883817.2883843 + + + + + + ArifM. Fareed + LarrazDaniel + EcheverriaMitziu + ReynoldsAndrew + ChowdhuryOmar + TinelliCesare + + SYSLITE: Syntax-guided synthesis of PLTL formulas from finite traces + Formal methods in computer aided design, FMCAD + 2020 + 10.34727/2020/ISBN.978-3-85448-042-6_16 + + + + + + ReynoldsAndrew + BarbosaHaniel + NötzliAndres + BarrettClark W. + TinelliCesare + + cvc4sy: Smart and fast term enumeration for syntax-guided synthesis + Computer-aided verification, CAV + 2019 + 10.1007/978-3-030-25543-5_5 + + + + + + RahaRitam + RoyRajarshi + FijalkowNathanaël + NeiderDaniel + + Scalable anytime algorithms for learning fragments of linear temporal logic + TACAS (1) + Springer + 2022 + 13243 + 10.1007/978-3-030-99524-9_14 + 263 + 280 + + + + + + RoyRajarshi + NeiderDaniel + + Inferring properties in computation tree logic + CoRR + 2023 + abs/2310.13778 + https://doi.org/10.48550/arXiv.2310.13778 + 10.48550/ARXIV.2310.13778 + + + + + + RahaRitam + RoyRajarshi + FijalkowNathanaël + NeiderDaniel + PérezGuillermo A. + + Synthesizing efficiently monitorable formulas in metric temporal logic + CoRR + 2023 + abs/2310.17410 + https://doi.org/10.48550/arXiv.2310.17410 + 10.48550/ARXIV.2310.17410 + + + + +