"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:64c3560c-8d59-48d4-807e-d4d03e6fd1ad","http://resolver.tudelft.nl/uuid:64c3560c-8d59-48d4-807e-d4d03e6fd1ad","Learning Driving Behavior by Timed Syntactic Pattern Recognition","Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.","","2011","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 of the common syntactic pattern recognition approach of sampling the signal values at a fixed rate, we model the time constraints using timed models. We learn these models using the RTI+ algorithm from grammatical inference, and show how to use computational mechanics and a form of semi-supervised classification to construct a real-time automaton classifier for driving behavior. Promising results are shown using this new approach.","","en","conference paper","AAAI Press/International Joint Conferences on Artificial Intelligence","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""
"uuid:eb9773dc-1981-40e4-95d6-9c95ecdaa2a5","http://resolver.tudelft.nl/uuid:eb9773dc-1981-40e4-95d6-9c95ecdaa2a5","A likelihood-ratio test for identifying probabilistic deterministic real-time automata from positive data (extended abstract)","Verwer, S.; De Weerdt, M.M.; Witteveen, C.","","2010","","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""
"uuid:3ec4fab1-946d-4b0b-87f2-c8ca092ebaeb","http://resolver.tudelft.nl/uuid:3ec4fab1-946d-4b0b-87f2-c8ca092ebaeb","Polynomial Distinguishability of Timed Automata (extended abstract)","Verwer, S.; De Weerdt, M.M.; Witteveen, C.","","2008","","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""
"uuid:4c8cc787-6447-4346-9dbd-bcfaa9867596","http://resolver.tudelft.nl/uuid:4c8cc787-6447-4346-9dbd-bcfaa9867596","Efficiently learning simple timed automata","Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.","","2008","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, etc.","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""
"uuid:f0e05366-b676-4393-9419-0739a594064c","http://resolver.tudelft.nl/uuid:f0e05366-b676-4393-9419-0739a594064c","Efficiently learning timed system models from observations","Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.","","2008","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). Consequently, it enjoys a sound theoretical basis which can be used to prove properties of our algorithm. For example, while it has long been known that learning DFAs is NP-complete, it has been shown that DFAs can be learned in the limit from polynomial time and data (efficiently in the limit) using a state merging method.","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""
"uuid:daac7b7b-a28f-477d-b2cc-0db00507a4e2","http://resolver.tudelft.nl/uuid:daac7b7b-a28f-477d-b2cc-0db00507a4e2","An algorithm for learning real-time automata (extended abstract)","Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.","","2007","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 such a case, the DFA model is too limited. A variant of a DFA that includes the notion of time is called a timed automaton (TA). In this model, each symbol of a word occurs at a certain point in time. The execution of a TA depends not only on the type of symbol occurring, but also on the time that has elapsed since some previous symbol occurrence. We are interested in the problem of identifying such a time dependent system from a data sample. Full paper is published in: Proceedings of the Annual Belgian-Dutch Machine Learning Conference (Benelearn), 2007 See: http://resolver.tudelft.nl/uuid:a202b4cf-5153-4ad5-b41d-5d0332bf04f2","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""
"uuid:a202b4cf-5153-4ad5-b41d-5d0332bf04f2","http://resolver.tudelft.nl/uuid:a202b4cf-5153-4ad5-b41d-5d0332bf04f2","An algorithm for learning real-time automata","Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.","","2007","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 deterministic finite automata. In addition to state merges, our algorithm can perform state splits by making use of the time values in the input data. We tested our learning algorithm on randomly generated problems. The results are promising and show that learning a real-time automaton directly from timed data outperforms a method that uses sampling in order to deal with the timed data.","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""
"uuid:fbb832cf-fde3-4435-a186-05692f3cd057","http://resolver.tudelft.nl/uuid:fbb832cf-fde3-4435-a186-05692f3cd057","On the identifiability in the limit of timed automata","Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.","","2006","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 important, a DFA is too limited. For example, it is impossible to distinguish between events that occur quickly after each other, and events that occur after each other with a significant delay between them. Consequently, we would like a model that can also deal with time information.","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""
"uuid:281696bd-623b-4a46-b187-38589a6362ec","http://resolver.tudelft.nl/uuid:281696bd-623b-4a46-b187-38589a6362ec","Identifying an Automation Model for Timed data (extended abstract)","Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.","","2006","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","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""
"uuid:faab7982-46bf-4d52-8a2a-324a88542584","http://resolver.tudelft.nl/uuid:faab7982-46bf-4d52-8a2a-324a88542584","Identifying an automaton model for timed data","Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.","","2006","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 reduction, we show how the currently best learning algorithm for DFAs (state merging) can be adapted to deal with time information.","","en","conference paper","","","","","","","","","Electrical Engineering, Mathematics and Computer Science","Software Computer Technology","","","",""