Timed automata for behavioral pattern recognition

Sicco Verwer; Mathijs De Weerdt; Cees Witteveen

Timed automata for behavioral pattern recognition

Sicco Verwer^*, Mathijs De Weerdt, Cees Witteveen

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › Scientific › peer-review

Abstract

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 constructive 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 the inference of finite automata.

Original language	English
Pages (from-to)	291-296
Number of pages	6
Journal	Belgian/Netherlands Artificial Intelligence Conference
Publication status	Published - 1 Dec 2005
Event	17th Belgium-Netherlands Conference on Artificial Intelligence, BNAIC 2005 - Brussels, Belgium Duration: 17 Oct 2005 → 18 Oct 2005

Cite this

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title = "Timed automata for behavioral pattern recognition",

abstract = "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 constructive 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 the inference of finite automata.",

author = "Sicco Verwer and {De Weerdt}, Mathijs and Cees Witteveen",

year = "2005",

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language = "English",

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T1 - Timed automata for behavioral pattern recognition

AU - Verwer, Sicco

AU - De Weerdt, Mathijs

AU - Witteveen, Cees

PY - 2005/12/1

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N2 - 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 constructive 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 the inference of finite automata.

AB - 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 constructive 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 the inference of finite automata.

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M3 - Conference article

AN - SCOPUS:84873465727

SN - 1568-7805

SP - 291

EP - 296

JO - Belgian/Netherlands Artificial Intelligence Conference

JF - Belgian/Netherlands Artificial Intelligence Conference

T2 - 17th Belgium-Netherlands Conference on Artificial Intelligence, BNAIC 2005

Y2 - 17 October 2005 through 18 October 2005

ER -

Timed automata for behavioral pattern recognition

Abstract

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