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Residuality and Learning for Nondeterministic Nominal Automata [article]

Joshua Moerman, Matteo Sammartino
2022 arXiv   pre-print
This property enables active learning algorithms, and makes up for the fact that residuality -- a semantic property -- is undecidable for nominal automata.  ...  To answer it, we develop the theory of residual nominal automata, a subclass of nondeterministic nominal automata.  ...  We thank Borja Balle for references on residual probabilistic languages, and Henning Urbat for discussions on nominal lattice theory.  ... 
arXiv:1910.11666v7 fatcat:w2qd6tv4o5hypbhu62urhlluwu

Residuality and Learning for Nondeterministic Nominal Automata

Joshua Moerman, Matteo Sammartino
2022 Logical methods in computer science : LMCS 18(1)  
This property enables active learning algorithms, and makes up for the fact that residuality -a semantic property -is undecidable for nominal automata.  ...  To answer it, we develop the theory of residual nominal automata, a subclass of nondeterministic nominal automata.  ...  We thank Borja Balle for references on residual probabilistic languages, and Henning Urbat for discussions on nominal lattice theory.  ... 
doi:10.18154/rwth-2022-01916 fatcat:lyzgpncm7jcr5ijewbmsmknpey

Active Automata Learning in Practice [chapter]

Falk Howar, Bernhard Steffen
2018 Lecture Notes in Computer Science  
Five years ago (in 2011) we have surveyed the then current state of active automata learning research and applications of active automata learning in practice.  ...  Active automata learning is slowly becoming a standard tool in the toolbox of the software engineer.  ...  Angluin et al. develop learning algorithms for universal automata, and alternating automata [14] and evaluate the performance trade offs for inferring these automata models -compared to deterministic  ... 
doi:10.1007/978-3-319-96562-8_5 fatcat:jtpzftrzvzf3tdtbncntfc4s4q

Learning Probabilistic Automata Using Residuals [chapter]

Wenjing Chu, Shuo Chen, Marcello Bonsangue
2021 Lecture Notes in Computer Science  
We show that our method learns the structure of the automaton precisely for a class of probabilistic automata strictly including deterministic one and give some experimental results to compare the learned  ...  In this paper, we efficiently construct a probabilistic automaton from a sample by first learning its non-deterministic structure using residual languages and then assigning appropriate probabilities to  ...  Residual automata are non-deterministic automata that can be used to learn efficiently any regular language.  ... 
doi:10.1007/978-3-030-85315-0_17 fatcat:bkab53jdufdnheliepfqb6dppy

Angluin-Style Learning of NFA

Benedikt Bollig, Peter Habermehl, Carsten Kern, Martin Leucker
2009 International Joint Conference on Artificial Intelligence  
More specifically, residual finite-state automata (RFSA) are learned similarly as in Angluin's popular L * algorithm, which, however, learns deterministic finitestate automata (DFA).  ...  We introduce NL * , a learning algorithm for inferring non-deterministic finite-state automata using membership and equivalence queries.  ...  Learning of Residual Finite-State Automata We will now modify Angluin's learning algorithm L * , which infers DFA, towards learning of NFA in terms of RFSA.  ... 
dblp:conf/ijcai/BolligHKL09 fatcat:zbxujjnmpfdbngdo5ietawru3y

Generalization over different cellular automata rules learned by a deep feed-forward neural network [article]

Marcel Aach, Jens Henrik Goebbert, Jenia Jitsev
2021 arXiv   pre-print
Results show that the network is able to learn the rules of various, complex cellular automata and generalize to unseen configurations.  ...  Code to reproduce the experiments is publicly available at: https://github.com/SLAMPAI/generalization-cellular-automata  ...  The motivation of this paper is to study generalization on a problem setting of learning the rules underlying pattern generation in cellular automata (CA) [3, 4] .  ... 
arXiv:2103.14886v2 fatcat:htmk2dygzfcyzfpxvo3jttbwzu

Image segmentation via Cellular Automata [article]

Mark Sandler, Andrey Zhmoginov, Liangcheng Luo, Alexander Mordvintsev, Ettore Randazzo, Blaise Agúera y Arcas
2020 arXiv   pre-print
In this paper, we propose a new approach for building cellular automata to solve real-world segmentation problems.  ...  The update rule is applied repeatedly in parallel to a large random subset of cells and after convergence is used to produce segmentation masks that are then back-propagated to learn the optimal update  ...  An alternative view of cellular automata is to think of cellular automata as a recurrent neural network.  ... 
arXiv:2008.04965v2 fatcat:xhelbmst7fd2vbkdfbfsvzcere

Data aggregation in sensor networks using learning automata

Mehdi Esnaashari, M. R. Meybodi
2009 Wireless networks  
These learning automata in the network collectively learn the path of aggregation with maximum aggregation ratio for each node for transmitting its packets toward the sink.  ...  In this paper, a learning automata based data aggregation method in sensor networks when the environment's changes can not be predicted beforehand will be proposed.  ...  Learning Automata Learning automata is an abstract model which randomly selects one action out of its finite set of actions and performs it on a random environment.  ... 
doi:10.1007/s11276-009-0162-5 fatcat:wjznbm3w2zazzhnxu6rmkmgxqm

A Novel Approach for the Determination of Membership Values of the Strings in Fuzzy Languages

Rahul KumarSingh, Ajay Kumar
2014 International Journal of Computer Applications  
Classical automata theory can not deal with uncertainty.  ...  They explained complete residuated lattice, factor fuzzy automaton, alternate reduction, fuzzy relation equation, and state reduction.  ...  [11] explained determinization of fuzzy finite automata with membership degree using complete residuated lattice.  ... 
doi:10.5120/17548-8144 fatcat:qb7chkpssze5vfqsemrqroof3y

Learning register automata: from languages to program structures

Malte Isberner, Falk Howar, Bernhard Steffen
2013 Machine Learning  
This paper reviews the development of Register Automaton learning, an enhancement of active automata learning to deal with infinite-state systems.  ...  We are convinced that this development will significantly contribute to paving the road for active automata learning to become a technology of high practical importance.  ...  on the learning of Register Automata.  ... 
doi:10.1007/s10994-013-5419-7 fatcat:wz3atcp7vjcf7irzgejh45a2li

A Generic Algorithm for Learning Symbolic Automata from Membership Queries [chapter]

Oded Maler, Irini-Eleftheria Mens
2017 Lecture Notes in Computer Science  
These languages are accepted by deterministic symbolic automata that use predicates to label transitions, forming a finite partition of the alphabet for every state.  ...  Our learning algorithm, an adaptation of Angluin's L * , combines standard automaton learning by state characterization, with the learning of the static predicates that define the alphabet partitions.  ...  A Fig. 5 : 5 Symbolic automata and semantics function learned in Example 1.  ... 
doi:10.1007/978-3-319-63121-9_8 fatcat:t6f4ych6ineclnitmwffyyqyca

Cluster-based Coverage Scheme for Wireless Sensor Networks using Learning Automata

Ali Ghaffari, Seyyed Keyvan Mousavi
2021 Journal of Information Systems and Telecommunication  
In this paper, a cluster-based scheme for the coverage problem of WSNs using learning automata is proposed.  ...  In the proposed scheme, each node creates the action and probability vectors of learning automata for itself and its neighbors, then determines the status of itself and all its neighbors and finally sends  ...  In [26] , the authors proposed PCLA (Partial Coverage with Learning Automata), a novel algorithm that relies on learning automata to implement sleep scheduling approaches.  ... 
doi:10.52547/jist.9.35.197 fatcat:bzelbd6rznfqzpc3i2tnma3jxi

Methodology for monitoring and diagnosing faults of hybrid dynamic systems: a case study on a desalination plant

Achbi Mohammed Said, Kechida Sihem
2020 Diagnostyka  
This article presents an approach to the diagnosis of hybrid systems using hybrid automata and neural-fuzzy system.  ...  On the other hand, the hybrid automata gives a perfect estimate of the discrete events and make it possible to execute a fault detection algorithm mainly consists of classifying the appeared defects.  ...  With this model we can generate 5 residuals: 3 continuous residuals and 2 signature events residuals.  ... 
doi:10.29354/diag/116076 fatcat:arczzi7wcfeulpk6wvsj434uca

Page 8149 of Mathematical Reviews Vol. , Issue 2004j [page]

2004 Mathematical Reviews  
Ralf Kiisters and Thomas Wilke, Deciding the first level of the uw-calculus alternation hierarchy (241-252); Martin Leucker, P.  ...  Frank Drewes and Joost Engelfriet, Branching grammars: a generalization of ETOL systems (266-278); Frank Drewes and Jo- hanna Hoégberg, Learning a regular tree language from a teacher (279-291); Rudolf  ... 

Page 3108 of Psychological Abstracts Vol. 91, Issue 8 [page]

2004 Psychological Abstracts  
—The paper uses ideas from machine learning and artificial intelligence to provide the model of cellular automata based on rough set theory and the response to it in simulated cars.  ...  The standard ANN design with a polychotomous situation requires an output variable for each alternative.  ... 
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