Searched for:
(1 - 19 of 19)
document
van der Lee, Wesley (author), Verwer, S.E. (author)
Although the importance of mobile applications grows every day, recent vulnerability reports argue the application's deficiency to meet modern security standards. Testing strategies alleviate the problem by identifying security violations in software implementations. This paper proposes a novel testing methodology that applies state machine...
conference paper 2018
document
Zhang, Yihuan (author), Lin, Q. (author), Wang, Jun (author), Verwer, S.E. (author)
Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common state sequences are extracted from the model...
conference paper 2017
document
Verwer, S.E. (author), Zhang, Yingqian (author)
We encode the problem of learning the optimal decision tree of a given depth as an integer optimization problem. We show experimentally that our method (DTIP) can be used to learn good trees up to depth 5 from data sets of size up to 1000. In addition to being efficient, our new formulation allows for a lot of flexibility. Experiments show that...
conference paper 2017
document
Wieman, Rick (author), Finavaro Aniche, M. (author), Lobbezoo, Willem (author), Verwer, S.E. (author), van Deursen, A. (author)
Passive learning techniques infer graph models on the behavior of a system from large trace logs. The research community has been dedicating great effort in making passive learning techniques more scalable and ready to use by industry. However, there is still a lack of empirical knowledge on the usefulness and applicability of such techniques in...
conference paper 2017
document
Hammerschmidt, C.A. (author), Marchal, Samuel (author), State, Radu (author), Pellegrino, G. (author), Verwer, S.E. (author)
The task of network traffic monitoring has evolved drastically with the ever-increasing amount of data flowing in large scale networks. The automated analysis of this tremendous source of information often comes with using simpler models on aggregated data (e.g. IP flow records) due to time and space constraints. A step towards utilizing IP flow...
conference paper 2016
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
We advocate the use of an explicit time representation in syntactic pattern recognition because it can result in more succinct models and easier learning problems. We apply this approach to the real-world problem of learning models for the driving behavior of truck drivers. We discretize the values of onboard sensors into simple events. Instead...
conference paper 2011
document
Verwer, S. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
conference paper 2010
document
Heule, M.J.H. (author), Verwer, S. (author)
We present an exact algorithm for identification of deterministic finite automata (DFA) which is based on satisfiability (SAT) solvers. Despite the size of the low level SAT representation, our approach seems to be competitive with alternative techniques. Our contributions are threefold: First, we propose a compact translation of DFA...
conference paper 2009
document
Verwer, S. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
conference paper 2008
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
We describe an efficient algorithm for learning deterministic real-time automata (DRTA) from positive data. This data can be obtained from observations of the process to be modeled. The DRTA model we learn from such data can be used reason and gain knowledge about realtime systems such as network protocols, business processes, reactive systems,...
conference paper 2008
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
This paper describes an efficient algorithm for learning a timed model from observations. The algorithm is based on the state merging method for learning a deterministic finite state automaton (DFA). This method and its problem have been the subject of many studies within the grammatical inference field, see e.g. (de la Higuera, 2005)....
conference paper 2008
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
A common model for discrete event systems is a deterministic finite automaton (DFA). An advantage of this model is that it can be interpreted by domain experts. When observing a real-world system, however, there often is more information than just the sequence of discrete events: the time at which these events occur may be very important. In...
conference paper 2007
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe state-merging algorithm for the problem of learning...
conference paper 2007
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
We are interested in identifying a model for discrete event systems from observations. A common way to model discrete event systems is by using deterministic finite state automata (DFA). When observing a system, however, there often is information in addition to the system events, namely, their times of occurrence. If this time information is...
conference paper 2006
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
In our paper we focus on learning systems of which the execution is determined by a finite set of discrete events. The full version of this paper appeared in: Proceedings of the 15th Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn 2006): http://resolver.tudelft.nl/uuid:faab7982-46bf-4d52-8a2a-324a88542584
conference paper 2006
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Witteveen, C. (author)
A model for discrete event systems (DES) can be learned from observations. We propose a simple type of timed automaton to model DES where the timing of the events is important. Learning such an automaton is proven to be NP-complete by a reduction from the problem of learning deterministic finite state automata (DFA) without time. Based on this...
conference paper 2006
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Witeveen, C. (author)
We argue that timed models are a suitable framework for the detection of behavior in real-world event systems. A timed model which detects behavior is constructible by a domain expert. The inference of these timed models from data is a hard problem. We prove the inference of a class of timed automata (event recording automata) to be harder than...
conference paper 2005
document
Verwer, S.E. (author), De Weerdt, M.M. (author), Zutt, J. (author)
In this interactive event we demonstrate a web-based software tool to teach theorem proving in propositional logic, called Bop. This tool is a proof editor in the Fitch proof system that can give hints, proofsteps, or even complete proofs to the student.
conference paper 2005
document
Van Vliet, L.J. (author), Verwer, B.J.H. (author)
conference paper 1988
Searched for:
(1 - 19 of 19)