19 Model Checking

Berg, Therese; Raffelt, Harald

doi:10.1007/11498490_25

Cited by 10 publications

(27 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Proof: This follows directly from the proof of correctness and complexity of Rivest and Schapire's algorithm [4,22]. Notice that the equivalence query cannot return a counterexample whose normal form uses more than k registers, as such a word is rejected by both A C (by definition) and by A O , (by construction).…”

Section: Learning Session Automatamentioning

confidence: 99%

“…Since A C only accepts symbolic words which are in normal form, a membership query for a given symbolic word u not in normal form will be answered negatively (without consulting the teacher); otherwise, the teacher will be given one data word included in γ(u) (all the answers on words of γ(u) are the same). Likewise, before submitting an equivalence query to the teacher, the learning algorithm checks if the current hypothesis automaton accepts symbolic words not in normal form 4 . If yes, one of those is taken as a counterexample, else an equivalence query is submitted to the teacher.…”

Section: Learning Session Automatamentioning

confidence: 99%

“…Let N be its number of states, K be the size of Σ ×Γ k and M the length of the longest counterexample returned by an equivalence query. Then, the learning algorithm for A terminates with at most O(KN 2 + N log(M )) membership and O(N ) equivalence queries.Proof: This follows directly from the proof of correctness and complexity of Rivest and Schapire's algorithm [4,22]. Notice that the equivalence query cannot return a counterexample whose normal form uses more than k registers, as such a word is rejected by both A C (by definition) and by A O , (by construction).…”

mentioning

confidence: 99%

“…Here we describe a variant of Rivest and Schapire's [22] algorithm which is itself a variant of Angluin's L * algorithm [2]. An overview of learning algorithms for deterministic finite state automata can be found, for example, in [4].The algorithm is based on the notion of observation table which contains the information accumulated by the learner during the learning process. An observation table over a given alphabet Σ × Γ k is a triple O = (T, U, V ) with U, V two sets of words over Σ × Γ k such that ε ∈ U ∩ V and T is a mapping (U ∪ U ·(Σ ×Γ k ))×V → {+, −}.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 4 more Smart Citations

A Fresh Approach to Learning Register Automata

Bollig

Habermehl

Leucker

et al. 2013

Developments in Language Theory

View full text Add to dashboard Cite

Abstract. This paper provides an Angluin-style learning algorithm for a class of register automata supporting the notion of fresh data values. More specifically, we introduce session automata which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. We show that session automata (i) have an expressiveness partly extending, partly reducing that of register automata, (ii) admit a symbolic regular representation, and (iii) have a decidable equivalence and model-checking problem (unlike register automata). Using these results, we establish a learning algorithm to infer session automata through membership and equivalence queries. Finally, we strengthen the robustness of our automaton by its characterization in monadic second-order logic. IntroductionLearning automata deals with the inference of automata based on some partial information, for example samples, which are words that either belong to their accepted language or not. A popular framework is that of active learning defined by Angluin [2] in which a learner may consult a teacher for so-called membership and equivalence queries to eventually infer the automaton in question. Learning automata has a lot of applications in computer science. Notable examples are the use in model checking [12] and testing [3]. See [18] for an overview.While active learning of regular languages is meanwhile well understood and is supported by freely available libraries such as learnlib [19] and libalf [8], extensions beyond plain regular languages are still an area of active research. Recently, automata dealing with potentially infinite data as first class citizens have been studied. Seminal works in this area are that of [1,15] and [14]. While the first two use abstraction and refinement techniques to cope with infinite data, the second approach learns a sub-class of register automata.In this paper, we follow the work on learning register automata. However, we study a different model than [14], having the ability to require that input data is fresh in the sense that it has not been seen so far. This feature has been proposed in [24] in the context of semantics of programming languages, as, forThis work is partially supported by EGIDE/DAAD-Procope (LeMon).example, fresh names are needed to model object creation in object-oriented languages. Moreover, fresh data values are important ingredients in modeling security protocols which often make use of so-called fresh nonces to achieve their security assertions [17]. Finally, fresh names are also important in the field of network protocols and are one of the key ingredients of the π-calculus [20].In general, the equivalence problem of register automata is undecidable (even without freshness). This limits their applicability in active learning, as equivalence queries cannot be implemented (correctly and completely). Therefore, we restrict the studied automaton model to either store fresh data values or read data values from registers. In the termi...

show abstract

Section: Learning Session Automatamentioning

confidence: 99%

Section: Learning Session Automatamentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

A Fresh Approach to Learning Register Automata

Bollig

Habermehl

Leucker

et al. 2013

Developments in Language Theory

View full text Add to dashboard Cite

show abstract

A Generic Algorithm for Learning Symbolic Automata from Membership Queries

Maler

Mens

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Learning and Integration of Parameterized Components Through Testing

Shahbaz

2007

Testing of Software and Communicating Systems

View full text Add to dashboard Cite

Abstract.We investigate the use of parameterized state machine models to drive integration testing, in the case where the models of components are not available beforehand. Therefore, observations from tests are used to learn partial models of components, from which further tests can be derived for integration. We have extended previous algorithms to the case of finite state models with predicates on input parameters and observable non-determinism. We also propose a new strategy where integration tests can be derived from the data collected during the learning process. Our work typically addresses the problem of assembling telecommunication services from black box COTS.

show abstract

19 Model Checking

Cited by 10 publications

References 0 publications

A Fresh Approach to Learning Register Automata

A Fresh Approach to Learning Register Automata

A Generic Algorithm for Learning Symbolic Automata from Membership Queries

Learning and Integration of Parameterized Components Through Testing

Contact Info

Product

Resources

About