ROLL 1.0: \(\omega \)Regular Language Learning Library
Abstract
We present ROLL 1.0, an \(\omega \)regular language learning library with command line tools to learn and complement Büchi automata. This open source Java library implements all existing learning algorithms for the complete class of \(\omega \)regular languages. It also provides a learningbased Büchi automata complementation procedure that can be used as a baseline for automata complementation research. The tool supports both the Hanoi Omega Automata format and the BA format used by the tool RABIT. Moreover, it features an interactive Jupyter notebook environment that can be used for educational purpose.
1 Introduction
In her seminal work [3], Angluin introduced the wellknown algorithm \(\textsf {L}^*\) to learn regular languages by means of deterministic finite automata (DFAs). In the learning setting presented in [3], there is a teacher, who knows the target language L, and a learner, whose task is to learn the target language, represented by an automaton. The learner interacts with the teacher by means of two kinds of queries: membership queries and equivalence queries. A membership query \(\mathrm {MQ}(w)\) asks whether a string w belongs to L while an equivalence query \(\mathrm {EQ}(A)\) asks whether the conjectured DFA \(A\) recognizes L. The teacher replies with a witness if the conjecture is incorrect otherwise the learner completes its job. This learning setting now is widely known as active automata learning. In recent years, active automata learning algorithms have attracted increasing attention in the computer aided verification community: it has been applied in blackbox model checking [24], compositional verification [12], program verification [10], error localization [8], and model learning [26].
Due to the increasing importance of automata learning algorithms, many efforts have been put into the development of automata learning libraries such as libalf [6] and LearnLib [18]. However, their focus is only on automata accepting finite words, which correspond to safety properties. The \(\omega \)regular languages are the standard formalism to describe liveness properties. The problem of learning the complete class of \(\omega \)regular languages was considered open until recently, when it has been solved by Farzan et al. [15] and improved by Angluin et al. [4].
However, the research on applying \(\omega \)regular language learning algorithms for verification problems is still in its infancy. Learning algorithms for \(\omega \)regular languages are admittedly much more complicated than their finite regular language counterparts. This becomes a barrier for the researchers doing further investigations and experiments on such topics. We present ROLL 1.0, an opensource library implementing all existing learning algorithms for the complete class of \(\omega \)regular languages known in literature, which we believe can be an enabling tool for this direction of research. To the best of our knowledge, ROLL 1.0 is the only publicly available tool focusing on \(\omega \)regular language learning.
ROLL, a preliminary version of ROLL 1.0, was developed in [22] to compare the performance of different learning algorithms for Büchi automata (BAs). The main improvements made in ROLL 1.0 compared to its previous version are as follows. ROLL 1.0 rewrites the algorithms in the core part of ROLL and obtains high modularity to allow for supporting the learning algorithms for more types of \(\omega \)automata than just BAs, algorithms to be developed in future. In addition to the BA format [1, 2, 11], ROLL 1.0 now also supports the Hanoi Omega Automata (HOA) format [5]. Besides the learning algorithms, ROLL 1.0 also contains complementation [23] and a new language inclusion algorithm. Both of them are built on top of the BAs learning algorithms. Experiments [23] have shown that the resulting automata produced by the learningbased complementation can be much smaller than those built by structurebased algorithms [7, 9, 19, 21, 25]. Therefore, the learningbased complementation is suitable to serve as a baseline for Büchi automata complementation researches. The language inclusion checking algorithm implemented in ROLL 1.0 is based on learning and a Monte Carlo word sampling algorithm [17]. ROLL 1.0 features an interactive mode which is used in the ROLL Jupyter notebook environment. This is particularly helpful for teaching and learning how \(\omega \)regular language learning algorithms work.
2 ROLL 1.0 Architecture and Usage
Learning Library. The learning library implements all known BA learning algorithms for the full class of \(\omega \)regular languages: the \(L^{\$}\) learner [15], based on DFA learning [3], and the \(L^{\omega }\) learner [22], based on three canonical family of DFAs (FDFAs) learning algorithms [4, 22]. ROLL 1.0 supports both observation tables [3] and classification trees [20] to store membership query answers. All learning algorithms provided in ROLL 1.0 implement the Learner interface; their corresponding teachers implement the Teacher interface. Any Java object that implements Teacher and can decide the equivalence of two Büchi automata is a valid teacher for the BA learning algorithms. Similarly, any Java object implementing Learner can be used as a learner, making ROLL 1.0 easy to extend with new learning algorithms and functionalities. The BA teacher implemented in ROLL 1.0 uses RABIT [1, 2, 11] to answer the equivalence queries posed by the learners since the counterexamples RABIT provides tend to be short and hence are easier to analyze; membership queries are instead answered by implementing the ASCC algorithm from [16].
 Learning mode (command learn) makes ROLL 1.0 learn a Büchi automaton equivalent to the given Büchi automaton; this can be used, for instance, to get a possibly smaller BA. The default option for storing answers to membership queries is table, which selects the observation tables; classification trees can be chosen instead by means of the tree option. for instance runs ROLL 1.0 in learning mode against the input BA aut.hoa; it learns aut.hoa by means of the \(L^{\omega }\) learner using observation tables. The three canonical FDFA learning algorithms given in [4] can be chosen by means of the options syntactic (default), recurrent, and periodic. Options under (default) and over control which approximation is used in the \(L^{\omega }\) learner [22] to transform an FDFA to a BA. By giving the option ldollar, ROLL 1.0 switches to use the \(L^{\$}\) learner instead of the default \(L^{\omega }\) learner.

Interactive mode (command play) allows users to play as the teacher guiding ROLL 1.0 in learning the language they have in mind. To show how the learning procedure works, ROLL 1.0 outputs each intermediate result in the Graphviz dot layout format^{1}; users can use Graphviz’s tools to get a graphical view of the output BA so to decide whether it is the right conjecture.

Complementation (command complement) of the BA \(\mathcal {B}\) in ROLL 1.0 is based on the algorithm from [23] which learns the complement automaton \(\mathcal {B}^{\mathsf {c}}\) from a teacher who knows the language \(\varSigma ^{\omega }\setminus \mathcal {L}(\mathcal {B})\). This allows ROLL 1.0 to disentangle \(\mathcal {B}^{\mathsf {c}}\) from the structure of \(\mathcal {B}\), avoiding the \(\varOmega ((0.76n)^{n})\) blowup [27] of the structurebased complementation algorithms (see., e.g., [7, 19, 21, 25]).

Inclusion testing (command include) between two BAs \(\mathcal {A}\) and \(\mathcal {B}\) is implemented in ROLL 1.0 as follows: (1) first, sample several \(\omega \)words \(w \in \mathcal {L}(\mathcal {A})\) and check whether \(w \notin \mathcal {L}(\mathcal {B})\) to prove \(\mathcal {L}(\mathcal {A}) \not \subseteq \mathcal {L}(\mathcal {B})\); (2) then, try simulation techniques [11, 13, 14] to prove inclusion; (3) finally, use the learning based complementation algorithm to check inclusion. The ROLL 1.0’s \(\omega \)word sampling algorithm is an extension of the one proposed in [17]. The latter only samples paths visiting any state at most twice while ROLL 1.0’s variant allows for sampling paths visiting any state at most K times, where K is usually set to the number of states in \(\mathcal {A}\). In this way, ROLL 1.0 can get a larger set of \(\omega \)words accepted by \(\mathcal {A}\) than the set from the original algorithm.
Online availability of ROLL 1.0. ROLL 1.0 is an opensource library freely available online at https://iscasmc.ios.ac.cn/roll/, where more details are provided about its commands and options, its use as a Java library, and its GitHub repository^{2}. Moreover, from the roll page, it is possible to access an online Jupyter notebook^{3} allowing to interact with ROLL 1.0 without having to download and compile it. Each client gets a new instance of the notebook, provided by JupyterHub^{4}, so to avoid unexpected interactions between different users. Figure 2 shows few screenshots of the notebook for learning in interactive mode the language \(\varSigma ^{*} \cdot b^{\omega }\) over the alphabet \(\varSigma = \{a, b\}\). As we can see, the membership query \(\mathrm {MQ}(w)\) is answered by means of the mqOracle function: it gets as input two finite words, the stem and the loop of the ultimately periodic word w, and it checks whether loop contains only b. Then one can create a BA learner with the oracle mqOracle, say the BA learner nbaLearner, based on observation tables and the recurrent FDFAs, as shown in the topleft screenshot. One can check the internal table structures of nbaLearner by printing out the learner, as in the topright screenshot. The answer to an equivalence query is split in two parts: first, the call to getHypothesis() shows the currently conjectured BA; then, the call to refineHypothesis("ba", "ba") simulates a negative answer with counterexample \(ba \cdot (ba)^{\omega }\). After the refinement by nbaLearner, the new conjectured BA is already the right conjecture.
Footnotes
Notes
Acknowledgement
This work has been supported by the National Natural Science Foundation of China (Grants Nos. 61532019, 61761136011) and by the CAP project GZ1023.
References
 1.Abdulla, P.A., et al.: Simulation subsumption in Ramseybased Büchi automata universality and inclusion testing. In: Touili, T., Cook, B., Jackson, P. (eds.) CAV 2010. LNCS, vol. 6174, pp. 132–147. Springer, Heidelberg (2010). https://doi.org/10.1007/9783642142956_14CrossRefGoogle Scholar
 2.Abdulla, P.A., et al.: Advanced Ramseybased Büchi automata inclusion testing. In: Katoen, J.P., König, B. (eds.) CONCUR 2011. LNCS, vol. 6901, pp. 187–202. Springer, Heidelberg (2011). https://doi.org/10.1007/9783642232176_13CrossRefGoogle Scholar
 3.Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987)MathSciNetCrossRefGoogle Scholar
 4.Angluin, D., Fisman, D.: Learning regular omega languages. Theoret. Comput. Sci. 650, 57–72 (2016)MathSciNetCrossRefGoogle Scholar
 5.Babiak, T., et al.: The Hanoi OmegaAutomata format. In: Kroening, D., Păsăreanu, C.S. (eds.) CAV 2015. LNCS, vol. 9206, pp. 479–486. Springer, Cham (2015). https://doi.org/10.1007/9783319216904_31CrossRefGoogle Scholar
 6.Bollig, B., Katoen, J.P., Kern, C., Leucker, M., Neider, D., Piegdon, D.R.: libalf: the automata learning framework. In: Touili, T., Cook, B., Jackson, P. (eds.) CAV 2010. LNCS, vol. 6174, pp. 360–364. Springer, Heidelberg (2010). https://doi.org/10.1007/9783642142956_32CrossRefGoogle Scholar
 7.Büchi, J.R.: On a decision method in restricted second order arithmetic. In: International Congress on Logic, Methodology and Philosophy of Science, pp. 1–11 (1962)Google Scholar
 8.Chapman, M., Chockler, H., Kesseli, P., Kroening, D., Strichman, O., Tautschnig, M.: Learning the language of error. In: Finkbeiner, B., Pu, G., Zhang, L. (eds.) ATVA 2015. LNCS, vol. 9364, pp. 114–130. Springer, Cham (2015). https://doi.org/10.1007/9783319249537_9CrossRefGoogle Scholar
 9.Chen, Y.F., et al.: Advanced automatabased algorithms for program termination checking. In: PLDI, pp. 135–150 (2018)Google Scholar
 10.Chen, Y.F., et al.: PAC learningbased verification and model synthesis. In: ICSE, pp. 714–724 (2016)Google Scholar
 11.Clemente, L., Mayr, R.: Advanced automata minimization. In: POPL, pp. 63–74 (2013)Google Scholar
 12.Cobleigh, J.M., Giannakopoulou, D., Păsăreanu, C.S.: Learning assumptions for compositional verification. In: Garavel, H., Hatcliff, J. (eds.) TACAS 2003. LNCS, vol. 2619, pp. 331–346. Springer, Heidelberg (2003). https://doi.org/10.1007/354036577X_24CrossRefzbMATHGoogle Scholar
 13.Dill, D.L., Hu, A.J., WongToi, H.: Checking for language inclusion using simulation preorders. In: Larsen, K.G., Skou, A. (eds.) CAV 1991. LNCS, vol. 575, pp. 255–265. Springer, Heidelberg (1992). https://doi.org/10.1007/3540551794_25CrossRefGoogle Scholar
 14.Etessami, K., Wilke, T., Schuller, R.A.: Fair simulation relations, parity games, and state space reduction for Büchi automata. In: Orejas, F., Spirakis, P.G., van Leeuwen, J. (eds.) ICALP 2001. LNCS, vol. 2076, pp. 694–707. Springer, Heidelberg (2001). https://doi.org/10.1007/3540482245_57CrossRefzbMATHGoogle Scholar
 15.Farzan, A., Chen, Y.F., Clarke, E.M., Tsay, Y.K., Wang, B.Y.: Extending automated compositional verification to the full class of omegaregular languages. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 2–17. Springer, Heidelberg (2008). https://doi.org/10.1007/9783540788003_2CrossRefzbMATHGoogle Scholar
 16.Gaiser, A., Schwoon, A.: Comparison of algorithms for checking emptiness of Büchi automata. In: MEMICS (2009)Google Scholar
 17.Grosu, R., Smolka, S.A.: Monte carlo model checking. In: Halbwachs, N., Zuck, L.D. (eds.) TACAS 2005. LNCS, vol. 3440, pp. 271–286. Springer, Heidelberg (2005). https://doi.org/10.1007/9783540319801_18CrossRefGoogle Scholar
 18.Isberner, M., Howar, F., Steffen, B.: The opensource LearnLib. In: Kroening, D., Păsăreanu, C.S. (eds.) CAV 2015. LNCS, vol. 9206, pp. 487–495. Springer, Cham (2015). https://doi.org/10.1007/9783319216904_32CrossRefGoogle Scholar
 19.Kähler, D., Wilke, T.: Complementation, disambiguation, and determinization of Büchi automata unified. In: Aceto, L., Damgård, I., Goldberg, L.A., Halldórsson, M.M., Ingólfsdóttir, A., Walukiewicz, I. (eds.) ICALP 2008. LNCS, vol. 5125, pp. 724–735. Springer, Heidelberg (2008). https://doi.org/10.1007/9783540705758_59CrossRefzbMATHGoogle Scholar
 20.Kearns, M.J., Vazirani, U.V.: An Introduction to Computational Learning Theory. MIT Press, Cambridge (1994)CrossRefGoogle Scholar
 21.Kupferman, O., Vardi, M.Y.: Weak alternating automata are not that weak. ACM Trans. Comput. Logic 2(3), 408–429 (2001)MathSciNetCrossRefGoogle Scholar
 22.Li, Y., Chen, Y.F., Zhang, L., Liu, D.: A novel learning algorithm for Büchi automata based on family of DFAs and classification trees. In: Legay, A., Margaria, T. (eds.) TACAS 2017. LNCS, vol. 10205, pp. 208–226. Springer, Heidelberg (2017). https://doi.org/10.1007/9783662545775_12CrossRefGoogle Scholar
 23.Li, Y., Turrini, A., Zhang, L., Schewe, S.: Learning to complement Büchi automata. In: Dillig, I., Palsberg, J. (eds.) VMCAI 2018. LNCS, vol. 10747, pp. 313–335. Springer, Cham (2018). https://doi.org/10.1007/9783319737218_15CrossRefGoogle Scholar
 24.Peled, D., Vardi, M.Y., Yannakakis, M.: Black box checking. J. Autom. Lang. Comb. 7(2), 225–246 (2001)MathSciNetzbMATHGoogle Scholar
 25.Piterman, N.: From nondeterministic Büchi and Streett automata to deterministic parity automata. In: LICS, pp. 255–264 (2006)Google Scholar
 26.Vaandrager, F.: Model learning. Commun. ACM 60(2), 86–95 (2017)CrossRefGoogle Scholar
 27.Yan, Q.: Lower bounds for complementation of omegaautomata via the full automata technique. Logical Methods Comput. Sci. 4(1) (2008)Google Scholar
Copyright information
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.