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Testing Learning of Restarting Automata using Genetic Algorithm
Kovářová, Lenka ; Mráz, František (advisor) ; Černo, Peter (referee)
Title: Testing the Learning of Restarting Automata using Genetic Algorithm Author: Bc. Lenka Kovářová Department: Department of Software and Computer Science Education Supervisor: RNDr. František Mráz, CSc. Abstract: Restarting automaton is a theoretical model of device recognizing a formal language. The construction of various versions of restarting automata can be a hard work. Many different methods of learning such automata have been developed till now. Among them are also methods for learning target restarting automaton from a finite set of positive and negative samples using genetic algorithms. In this work, we propose a method for improving learning of restarting automata by means of evolutionary algorithms. The improvement consists in inserting new rules of a special form enabling adaption of the learning algorithm to the particular language. Furthermore, there is proposed a system for testing of learning algorithms for restarting automata supporting especially learning by evolutionary algorithms. A part of the work is a program for learning restarting automata using the proposed new method with a subsequent testing of discovered automata and its evaluation in a graphic form mainly. Keywords: machine learning, grammatical inference, restarting automata, genetic algorithms
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Restricted Restarting Automata
Černo, Peter ; Mráz, František (advisor) ; Kutrib, Martin (referee) ; Průša, Daniel (referee)
Restarting automata were introduced as a model for analysis by reduction which is a linguistically motivated method for checking correctness of a sentence. The thesis studies locally restricted models of restarting automata which (to the contrary of general restarting automata) can modify the input tape based only on a limited context. The investigation of such restricted models is easier than in the case of general restarting automata. Moreover, these models are effectively learnable from positive samples of reductions and their instructions are human readable. Powered by TCPDF (www.tcpdf.org)
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Machine learning of analysis by reduction
Hoffmann, Petr ; Mráz, František (advisor) ; Otto, Friedrich (referee) ; Průša, Daniel (referee)
We study the inference of models of the analysis by reduction that forms an important tool for parsing natural language sentences. We prove that the inference of such models from positive and negative samples is NP-hard when requiring a small model. On the other hand, if only positive samples are considered, the problem is effectively solvable. We propose a new model of the analysis by reduction (the so-called single k-reversible restarting automaton) and propose a method for inferring it from positive samples of analyses by reduction. The power of the model lies between growing context-sensitive languages and context-sensitive languages. Benchmarks using targets based on grammars have several drawbacks. Therefore we propose a benchmark working with targets based on random automata, that can be used to evaluate inference algorithms. This benchmark is then used to evaluate our inference method. 1
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Testing Learning of Restarting Automata using Genetic Algorithm
Kovářová, Lenka ; Mráz, František (advisor) ; Černo, Peter (referee)
Title: Testing the Learning of Restarting Automata using Genetic Algorithm Author: Bc. Lenka Kovářová Department: Department of Software and Computer Science Education Supervisor: RNDr. František Mráz, CSc. Abstract: Restarting automaton is a theoretical model of device recognizing a formal language. The construction of various versions of restarting automata can be a hard work. Many different methods of learning such automata have been developed till now. Among them are also methods for learning target restarting automaton from a finite set of positive and negative samples using genetic algorithms. In this work, we propose a method for improving learning of restarting automata by means of evolutionary algorithms. The improvement consists in inserting new rules of a special form enabling adaption of the learning algorithm to the particular language. Furthermore, there is proposed a system for testing of learning algorithms for restarting automata supporting especially learning by evolutionary algorithms. A part of the work is a program for learning restarting automata using the proposed new method with a subsequent testing of discovered automata and its evaluation in a graphic form mainly. Keywords: machine learning, grammatical inference, restarting automata, genetic algorithms
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