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Evolving classification of agents' behaviours : a general approach.

Research output: Contribution to journalJournal article

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Evolving classification of agents' behaviours : a general approach. / Iglesias, Jose Antonio; Angelov, Plamen; Ledezma, Agapito; Sanchis, Araceli.

In: Evolving Systems, Vol. 1, No. 3, 10.2010, p. 161-171.

Research output: Contribution to journalJournal article

Harvard

Iglesias, JA, Angelov, P, Ledezma, A & Sanchis, A 2010, 'Evolving classification of agents' behaviours : a general approach.', Evolving Systems, vol. 1, no. 3, pp. 161-171. https://doi.org/10.1007/s12530-010-9008-8

APA

Iglesias, J. A., Angelov, P., Ledezma, A., & Sanchis, A. (2010). Evolving classification of agents' behaviours : a general approach. Evolving Systems, 1(3), 161-171. https://doi.org/10.1007/s12530-010-9008-8

Vancouver

Author

Iglesias, Jose Antonio ; Angelov, Plamen ; Ledezma, Agapito ; Sanchis, Araceli. / Evolving classification of agents' behaviours : a general approach. In: Evolving Systems. 2010 ; Vol. 1, No. 3. pp. 161-171.

Bibtex

@article{47afbef19f7744748fd759a3f24402b4,
title = "Evolving classification of agents' behaviours : a general approach.",
abstract = "By recognizing the behavior of others, many different tasks can be performed, such as to predict their future behavior, to coordinate with them or to assist them. If this behavior recognition can be done automatically, it can be very useful in many applications. However, an agents{\textquoteright} behavior is not necessarily fixed but rather it evolves/changes. Thus, it is essential to take into account these changes in any behavior recognition system. In this paper, we present a general approach to the classification of streaming data which represent a specific agent behavior based on evolving systems. The experiment results show that an evolving system based on our approach can efficiently model and recognize different behaviors in very different domains, in particular, UNIX command-line data streams, and intelligent home environments. (c) Springer",
keywords = "Evolving fuzzy systems, Agent modeling, Behavior classification",
author = "Iglesias, {Jose Antonio} and Plamen Angelov and Agapito Ledezma and Araceli Sanchis",
year = "2010",
month = oct
doi = "10.1007/s12530-010-9008-8",
language = "English",
volume = "1",
pages = "161--171",
journal = "Evolving Systems",
issn = "1868-6478",
publisher = "Springer Verlag",
number = "3",

}

RIS

TY - JOUR

T1 - Evolving classification of agents' behaviours : a general approach.

AU - Iglesias, Jose Antonio

AU - Angelov, Plamen

AU - Ledezma, Agapito

AU - Sanchis, Araceli

PY - 2010/10

Y1 - 2010/10

N2 - By recognizing the behavior of others, many different tasks can be performed, such as to predict their future behavior, to coordinate with them or to assist them. If this behavior recognition can be done automatically, it can be very useful in many applications. However, an agents’ behavior is not necessarily fixed but rather it evolves/changes. Thus, it is essential to take into account these changes in any behavior recognition system. In this paper, we present a general approach to the classification of streaming data which represent a specific agent behavior based on evolving systems. The experiment results show that an evolving system based on our approach can efficiently model and recognize different behaviors in very different domains, in particular, UNIX command-line data streams, and intelligent home environments. (c) Springer

AB - By recognizing the behavior of others, many different tasks can be performed, such as to predict their future behavior, to coordinate with them or to assist them. If this behavior recognition can be done automatically, it can be very useful in many applications. However, an agents’ behavior is not necessarily fixed but rather it evolves/changes. Thus, it is essential to take into account these changes in any behavior recognition system. In this paper, we present a general approach to the classification of streaming data which represent a specific agent behavior based on evolving systems. The experiment results show that an evolving system based on our approach can efficiently model and recognize different behaviors in very different domains, in particular, UNIX command-line data streams, and intelligent home environments. (c) Springer

KW - Evolving fuzzy systems

KW - Agent modeling

KW - Behavior classification

U2 - 10.1007/s12530-010-9008-8

DO - 10.1007/s12530-010-9008-8

M3 - Journal article

VL - 1

SP - 161

EP - 171

JO - Evolving Systems

JF - Evolving Systems

SN - 1868-6478

IS - 3

ER -