Exact finite-state machine identification from scenarios and temporal properties

Ulyantsev, Vladimir; Buzhinsky, Igor; Shalyto, Anatoly

doi:10.1007/s10009-016-0442-1

Cited by 26 publications

(25 citation statements)

References 37 publications

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“…6 Note, however, that our algorithms perform better in terms of execution time than approaches that solve exact identification problems. For example, [47] report experiments where learning a Mealy machine of 18 states requires more than 29 hours. The majority of the execution time here is spent in proving that there exists no machine with fewer than 18 states which is also consistent with the examples.…”

Section: Experimental Comparisonmentioning

confidence: 99%

Learning Moore machines from input–output traces

Giantamidis

Tripakis

Basagiannis

2019

Int J Softw Tools Technol Transfer

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The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample. arXiv:1605.07805v2 [cs.FL] 2 Sep 2016research on grammatical inference [15] which has studied similar, but not exactly the same problems, such as learning deterministic finite automata (DFA), which are special cases of Moore machines with a binary output, or subsequential transducers, which are more general than Moore machines.Our contributions are the following:1. We define formally the LMoMIO problem (learning Moore machines from input-output traces). Apart from the correctness criterion of consistency (that the learned machine be consistent with the given traces) we also introduce several performance criteria including size and accuracy of the learned machine, and computational complexity of the learning algorithm. 2. We adapt the notion of characteristic sample, which is known for DFA [15], to the case of Moore machines.Intuitively, a characteristic sample of a machine M is a set of traces which contains enough information to "reconstruct" M . The characteristic sample requirement (CSR) states that, when given as input a characteristic sample, the learning algorithm must produce a machine equivalent to the one that produced the sample. CSR is important, as it ensures identification in the limit: this is a key concept in automata learning theory which ensures that the learning algorithm will eventually learn the right machine when provided with a sufficiently large set of examples [18]. 3. We develop three algorithms to solve the LMoMIO problem, and analyze them in terms of computational complexity and other properties. We show that although all three algorithms guarantee consistency, only the most advanced among them, called MooreMI, satisfies the characteristic sample requirement. We also show that MooreMI achieves identification in the limit. 4. We report on a prototype implementation of all three algorithms and experimental results. The experiments show that Moor...

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Section: Experimental Comparisonmentioning

confidence: 99%

Learning Moore machines from input–output traces

Giantamidis

Tripakis

Basagiannis

2019

Int J Softw Tools Technol Transfer

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“…The works [6]- [8] are among the ones which propose solutions for this problem along with the tools which implement them. Then, note that operator X allows representing behavior traces in LTL, but for the purpose of computational efficiency it is possible to consider them separately [9]. Finally, there are approaches, such as [10], which treat behavior traces as the only kind of input data.…”

Section: Basic Block Modelsmentioning

confidence: 99%

“…On each iteration, the analyst first attempts to synthesize the models which comply with L (line 3). Since multiple synthesis tools are known [6]- [9], different tools can be used to obtain different models. In this paper, we use the following tools: Unbeast (https://www.react.uni-saarland.de/tools/unbeast), G4LTL-ST (https://sourceforge.net/projects/g4ltl), BoSy (https://github.com/reactive-systems/bosy), EFSM-Tools (https://github.com/ulyantsev/EFSM-tools).…”

Section: Workflowmentioning

confidence: 99%

Synthesis-Aided Reliability Assurance of Basic Block Models for Model Checking Purposes

Buzhinsky

Pakonen

Vyatkin

2018

2018 IEEE 27th International Symposium on Industrial Electronics (ISIE)

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In the Finnish nuclear industry, model checking, a formal verification technique, is used as an additional means of safety assurance for instrumentation and control (I&C) system design. Since the code of vendor-specific basic function blocks used in I&C is commonly closed, these blocks need to be modeled manually based on available specification. This modeling introduces an additional source of human factor into the verification process. To increase the reliability of the library of basic blocks used in nuclear I&C verification, we apply formal synthesis techniques, which can construct finite-state models of reactive systems from behavior examples and temporal properties. Since these techniques have computational limitations and synthesized models are hard to understand even by an analyst, we do not use them in the final verification process. Instead, in an iterative process, behavioral differences between a synthesized model and a manual model implementation are identified and used to create a list of features of manual implementations which either violate the specification or show that the specification is ambiguous.

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Section: Experimental Comparisonmentioning

confidence: 99%

“…Scenarios can be provided in various forms, e.g. message sequence charts [5], event sequence charts [24], or simply, input-output examples [47]. Requirements can be temporal logic formulas as in [5,47], or other types of constraints such as the scenario constraints used in [24].…”

Section: Introductionmentioning

confidence: 99%

Learning Moore Machines from Input-Output Traces

Giantamidis

Tripakis

2016

Lecture Notes in Computer Science

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Abstract. The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample.

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Exact finite-state machine identification from scenarios and temporal properties

Cited by 26 publications

References 37 publications

Learning Moore machines from input–output traces

Learning Moore machines from input–output traces

Synthesis-Aided Reliability Assurance of Basic Block Models for Model Checking Purposes

Learning Moore Machines from Input-Output Traces

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