A New Approach for Active Automata Learning Based on Apartness

Vaandrager, Frits; Garhewal, Bharat; Rot, Jurriaan; Wißmann, Thorsten

doi:10.1007/978-3-030-99524-9_12

Cited by 24 publications

(11 citation statements)

References 59 publications

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“…While L * has seen major improvements over the years and has inspired numerous variations for different types of transition systems, all approaches remain in common their focus on the equivalence of observations. The recently presented L ♯ algorithm [154] takes a different perspective: it instead focuses on apartness, a constructive form of inequality. L ♯ does not require data-structures such as observation tables or discrimination trees, instead operating directly on tree-shaped automata.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Guarded Kleene Algebra with Tests: Automata Learning

Zetzsche¹,

Silva²,

Sammartino³

2023

Electronic Notes in Theoretical Informatics and Computer Science

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Guarded Kleene Algebra with Tests (GKAT) is the fragment of Kleene Algebra with Tests (KAT) that arises by replacing the union and iteration operations of KAT with predicate-guarded variants. GKAT is more efficiently decidable than KAT and expressive enough to model simple imperative programs, making it attractive for applications to e.g. network verification. In this paper, we further explore GKAT's automata theory, and present GL*, an algorithm for learning the GKAT automaton representation of a black-box, by observing its behaviour. A complexity analysis shows that it is more efficient to learn a representation of a GKAT program with GL* than with Angluin's existing L* algorithm. We implement GL* and L* in OCaml and compare their performances on example programs.

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Section: Discussion and Future Workmentioning

confidence: 99%

Guarded Kleene Algebra with Tests: Automata Learning

Zetzsche¹,

Silva²,

Sammartino³

2023

Electronic Notes in Theoretical Informatics and Computer Science

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“…10 There is some resemblance between the partial matrix and the apartness relation of [VGRW22], used in query learning of DFAs.…”

Section: Efficient Identifiabilitymentioning

confidence: 99%

Inferring Symbolic Automata

Fisman¹,

Frenkel²,

Zilles³

2023

Logical Methods in Computer Science

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We study the learnability of symbolic finite state automata (SFA), a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the query learning paradigm, and so far all obtained results are positive. We provide a necessary condition for efficient learnability of SFAs in this paradigm, from which we obtain the first negative result. The main focus of our work lies in the learnability of SFAs under the paradigm of identification in the limit using polynomial time and data, and its strengthening efficient identifiability, which are concerned with the existence of a systematic set of characteristic samples from which a learner can correctly infer the target language. We provide a necessary condition for identification of SFAs in the limit using polynomial time and data, and a sufficient condition for efficient learnability of SFAs. From these conditions we derive a positive and a negative result. The performance of a learning algorithm is typically bounded as a function of the size of the representation of the target language. Since SFAs, in general, do not have a canonical form, and there are trade-offs between the complexity of the predicates on the transitions and the number of transitions, we start by defining size measures for SFAs. We revisit the complexity of procedures on SFAs and analyze them according to these measures, paying attention to the special forms of SFAs: normalized SFAs and neat SFAs, as well as to SFAs over a monotonic effective Boolean algebra. This is an extended version of the paper with the same title published in CSL'22.

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“…The MAT framework is an interesting abstraction to design algorithms and conduct proofs, and has been the basis for active model learning since its introduction (see e.g. L ⋆ [4], KV [24], TTT [22] or L # [33]). The teacher abstracts the system under learning (SUL), which complicates discussions on the practical interfaces between the learner and the SUL during applications.…”

Section: Preliminariesmentioning

confidence: 99%

“…We evaluate C 3AL in Section 4 using a broad range of experiments [27]. We compare several state-of-theart algorithms (namely L ⋆ [4], KV [24], TTT [22] and L # [33]) for targets of different sizes and different levels of noise, while varying the controllable parameters for both MAT and C 3AL. The experimental results show that in the case of noise, C 3AL allows us to drastically reduce the number of repeats required to learn correct models by handling some conflicts in the information it gathers from the system.…”

Section: Introductionmentioning

confidence: 99%

Conflict-Aware Active Automata Learning

Ferreira,

Henry,

Fernandes da Silva

et al. 2023

Electron. Proc. Theor. Comput. Sci.

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Active automata learning algorithms cannot easily handle conflict in the observation data (different outputs observed for the same inputs). This inherent inability to recover after a conflict impairs their effective applicability in scenarios where noise is present or the system under learning is mutating.We propose the Conflict-Aware Active Automata Learning (C 3AL) framework to enable handling conflicting information during the learning process. The core idea is to consider the so-called observation tree as a first-class citizen in the learning process. Though this idea is explored in recent work, we take it to its full effect by enabling its use with any existing learner and minimizing the number of tests performed on the system under learning, specially in the face of conflicts. We evaluate C 3AL in a large set of benchmarks, covering over 30 different realistic targets, and over 18,000 different scenarios. The results of the evaluation show that C 3AL is a suitable alternative framework for closed-box learning that can better handle noise and mutations.

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A New Approach for Active Automata Learning Based on Apartness

Abstract: We present $$L^{\#}$$ L # , a new and simple approach to active automata learning. Instead of focusing on equivalence of observations, like the $$L^{*}$$ L ∗ algorithm and its descendants, $$L^{\#}$$ L … Show more

Cited by 24 publications

References 59 publications

Guarded Kleene Algebra with Tests: Automata Learning

Guarded Kleene Algebra with Tests: Automata Learning

Inferring Symbolic Automata

Conflict-Aware Active Automata Learning

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