Learning Driving Behavior by Timed Syntactic Pattern Recognition

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...

A likelihood-ratio test for identifying probabilistic deterministic real-time automata from positive data (extended abstract)

Polynomial Distinguishability of Timed Automata (extended abstract)

Efficiently learning simple timed automata

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,...

Efficiently learning timed system models from observations

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)....

An algorithm for learning real-time automata (extended abstract)

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...

An algorithm for learning real-time automata

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...

On the identifiability in the limit of timed automata

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...

Identifying an Automation Model for Timed data (extended abstract)

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

Identifying an automaton model for timed data

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...

Timed Automata for Behavioral Pattern Recognition

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...

A tutoring system to practice theorem proving in Fitch

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.