From Passive to Active: Learning Timed Automata Efficiently

2020 ◽  
pp. 1-19
Author(s):  
Bernhard K. Aichernig ◽  
Andrea Pferscher ◽  
Martin Tappler
Keyword(s):  
Active Learning ◽  
Timed Automata

scholarly journals Learning Nondeterministic Real-Time Automata

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-26
Author(s):  
Jie An ◽  
Bohua Zhan ◽  
Naijun Zhan ◽  
Miaomiao Zhang
Keyword(s):  
Active Learning ◽  
Integrated Circuits ◽  
Real Time ◽  
Learning Algorithm ◽  
Timed Automata ◽  
Finite Automata ◽  
Special Kind ◽  
Learning Problem ◽  
Equivalence Queries

We present an active learning algorithm named NRTALearning for nondeterministic real-time automata (NRTAs). Real-time automata (RTAs) are a subclass of timed automata with only one clock which resets at each transition. First, we prove the corresponding Myhill-Nerode theorem for real-time languages. Then we show that there exists a unique minimal deterministic real-time automaton (DRTA) recognizing a given real-time language, but the same does not hold for NRTAs. We thus define a special kind of NRTAs, named residual real-time automata (RRTAs), and prove that there exists a minimal RRTA to recognize any given real-time language. This transforms the learning problem of NRTAs to the learning problem of RRTAs. After describing the learning algorithm in detail, we prove its correctness and polynomial complexity. In addition, based on the corresponding Myhill-Nerode theorem, we extend the existing active learning algorithm NL* for nondeterministic finite automata to learn RRTAs. We evaluate and compare the two algorithms on two benchmarks consisting of randomly generated NRTAs and rational regular expressions. The results show that NRTALearning generally performs fewer membership queries and more equivalence queries than the extended NL* algorithm, and the learnt NRTAs have much fewer locations than the corresponding minimal DRTAs. We also conduct a case study using a model of scheduling of final testing of integrated circuits.

Active Learning Through Use of Learning and Study Strategies

1990 ◽  
Vol 35 (5) ◽  
pp. 471-472
Author(s):  
Herbert J. Klausmeier
Keyword(s):  
Active Learning ◽  
Study Strategies ◽  
Learning And Study Strategies

A randomized controlled trial of an online, modular, active learning training program for behavioral activation for depression.

2017 ◽  
Vol 85 (8) ◽  
pp. 814-825 ◽  
Author(s):  
Ajeng J. Puspitasari ◽  
Jonathan W. Kanter ◽  
Andrew M. Busch ◽  
Rachel Leonard ◽  
Shira Dunsiger ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Active Learning ◽  
Training Program ◽  
Behavioral Activation ◽  
Controlled Trial ◽  
Randomized Controlled

Stimulating Active Learning in Aging and Diversity

2008 ◽  
Author(s):  
Lisa Wagner ◽  
Chandra M. Mehrotra
Keyword(s):  
Active Learning
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