Petr Grachev
ABSTRACT
The regular inference is one of the main problems of the formal language theory, which is to synthesize a finite-state automaton corresponding to some unknown regular language represented with a list of positive and negative examples. In this paper, we propose a new algorithm for regular inference along with special measures for evaluating quality of elements of automaton we call reputation. The algorithm belongs to genetic algorithms family and transforms candidate automatons based on the reputation of its elements. We prove effectiveness of our model by experiments on pregenerated datasets.
- Buzhinsky, I., Vyatkin, V. 2017. Automatic inference of finite-state plant models from traces and temporal properties. IEEE Transactions on Industrial Informatics, 13(4), 1521--1530.Hoare C.A.R., Wirth N. An axiomatic definition of the programming language PASCAL // Acta Informatica. - 1973. - Vol. 2, no. 4. - P. 335--355.Google Scholar
Cross Ref
- Buzhinsky, I., Vyatkin, V. 2017. Modular plant model synthesis from behavior traces and temporal properties. In 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1--7). IEEE.Google ScholarDigital Library
- Ovsiannikova, P., Chivilikhin, D., Ulyantsev, V., Shalyto, A. 2017. Closed-loop verification of a compensating group drive model using synthesized formal plant model. In 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1--4). IEEE.Google ScholarDigital Library
- Monteiro, J.C., Oliveira, A.L. 1998. Finite state machine decomposition for low power. In Proceedings of the 35th annual Design Automation Conference (pp. 758--763). ACM.Google ScholarDigital Library
- Barlović, R., Holfeld, A., Räbiger, J. 2008. Sampling in Semiconductor Manufacturing. Encyclopedia of Statistics in Quality and Reliability, 4.Google Scholar
- Smeenk, W., Moerman, J., Vaandrager, F., Jansen, D. N. 2015. Applying automata learning to embedded control software. In International Conference on Formal Engineering Methods (pp. 67--83). Springer, Cham.Google ScholarCross Ref
- Chivilikhin, D., Ulyantsev, V., Shalyto, A., Vyatkin, V. 2017. CSP-based inference of function block finite-state models from execution traces. In 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) (pp. 714--719). IEEE.Google ScholarCross Ref
- Sakarovitch, J. 2009. Elements of automata theory. Cambridge University Press.Google ScholarDigital Library
- Angluin, D. 1982. Inference of reversible languages. Journal of the ACM (JACM), 29(3), 741--765.Google ScholarDigital Library
- GGold, E.M. 1967. Language identification in the limit. Information and control, 10(5), 447--474.Google Scholar
- De la Higuera, C. 2010. Grammatical inference: learning automata and grammars. Cambridge University Press.Google Scholar
- Hingston, P. 2001. A genetic algorithm for regular inference. In Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (pp. 1299--1306). Morgan Kaufmann Publishers Inc.Google Scholar
- Parekh, R., Honavar, V. 2000. Grammar inference, automata induction, and language acquisition. Handbook of natural language processing, 727--764.Google Scholar
- Hutter, F., Kotthoff, L., Vanschoren, J. 2019. Automated Machine Learning-Methods, Systems, Challenges. Automated Machine Learning.Google Scholar
- Torrey, L., Shavlik, J. 2010. Transfer learning. In Handbook of research on machine learning applications and trends: algorithms, methods, and techniques (pp. 242--264). IGI Global.Google Scholar
- Peng, K. C., Wu, Z., Ernst, J. 2018. Zero-shot deep domain adaptation. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 764--781).Google ScholarDigital Library
- Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.Google ScholarDigital Library
- Shalamov, V., Filchenkov, A., Shalyto, A. 2019. Heuristic and metaheuristic solutions of pickup and delivery problem for self-driving taxi routing. Evolving Systems, 10(1), 3--11.Google ScholarCross Ref
- Buzdalova, A., Buzdalov, M. 2012. Increasing efficiency of evolutionary algorithms by choosing between auxiliary fitness functions with reinforcement learning. In 2012 11th International Conference on Machine Learning and Applications (Vol. 1, pp. 150--155). IEEE.Google ScholarDigital Library
- Wyard, P. 1994. Representational issues for context free grammar induction using genetic algorithms. In International Colloquium on Grammatical Inference (pp. 222--235). Springer, Berlin, Heidelberg.Google Scholar
- Petasis, G., Paliouras, G., Spyropoulos, C.D., alatsis, C. 2004. eg-GRIDS: Context-free grammatical inference from positive examples using genetic search. In International Colloquium on Grammatical Inference (pp. 223--234). Springer, Berlin, Heidelberg.Google Scholar
- Dunay, B.D., Petry, F.E., Buckles, B.P. 1994. Regular language induction with genetic programming. In Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence (pp. 396--400). IEEE.Google ScholarCross Ref
- Dupont, P. 1994. Regular grammatical inference from positive and negative samples by genetic search: the GIG method. In International Colloquium on Grammatical Inference (pp. 236--245). Springer, Berlin, Heidelberg.Google Scholar
- Grachev, P., Bezborodov, R., Smetannikov, I., Filchenkov, A. 2019. Exploring the Relationship between the Structural and the Actual Similarities of Automata. In Proceedings of the 3rd International Conference on Machine Learning and Soft Computing (pp. 81--86). ACM.Google ScholarDigital Library
- Tomita M. 1982. Learning of Construction of Finite Automata from Examples Using Hill-Climbing. RR: Regular Set Recognizer: tech. rep. / CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE.Google Scholar
Index Terms
- Reputational Genetic Model for Regular Inference
Recommendations
Grammar Inference Based on Passive Learning and Genetic Algorithm
ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft ComputingThe mathematical model of a deterministic finite automaton has a wide potential of application, for instance, in control systems. Some of that systems are not trivial and can be defined only in terms of formal language theory. In this paper, we propose ...
Exploring the Relationship between the Structural and the Actual Similarities of Automata
ICMLSC '19: Proceedings of the 3rd International Conference on Machine Learning and Soft ComputingOne of the most common representations of formal languages is deterministic finite-state automata. In recent years, a bunch of papers dedicated to the problem of synthesizing such an automaton by a list of positive and negative examples of some formal ...
Grammar Inference with Multiparameter Genetic Model
ICAIP '19: Proceedings of the 2019 3rd International Conference on Advances in Image ProcessingThe problem of regular inference is of interest in the formal language theory and its conjugates. In recent years, models have been proposed that solve this problem using machine learning methods. In this paper, we present a brand new model for regular ...
Comments