Applying bayesian networks for intelligent adaptable printing systems

Arjen Hommersom; Peter J.F. Lucas; René Waarsing; Pieter Koopman

Applying bayesian networks for intelligent adaptable printing systems

Arjen Hommersom^*
, Peter J.F. Lucas
, René Waarsing
, Pieter Koopman

^*Corresponding author for this work

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

Abstract

Adaptable systems are difficult to control as they have to dynamically adjust themselves at runtime to a changing environment, which is usually uncertain. Moreover, there is uncertainty about the underlying physical model of the system, which poses a problem for modelling the system's behaviour. In this paper, we propose to model the system using a Bayesian network, that can be learned, or tuned, from data. We demonstrate the usefulness of Bayesian networks for control by a case study in the area of adaptable printing systems and compare the approach with a classic PID controller.

Original language	English
Pages (from-to)	443-444
Number of pages	2
Journal	Belgian/Netherlands Artificial Intelligence Conference
Publication status	Published - 2009
Externally published	Yes
Event	21st Benelux Conference on Artificial Intelligence, BNAIC 2009 - Eindhoven, Netherlands Duration: 29 Oct 2009 → 30 Oct 2009 Conference number: 21

Cite this

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title = "Applying bayesian networks for intelligent adaptable printing systems",

abstract = "Adaptable systems are difficult to control as they have to dynamically adjust themselves at runtime to a changing environment, which is usually uncertain. Moreover, there is uncertainty about the underlying physical model of the system, which poses a problem for modelling the system's behaviour. In this paper, we propose to model the system using a Bayesian network, that can be learned, or tuned, from data. We demonstrate the usefulness of Bayesian networks for control by a case study in the area of adaptable printing systems and compare the approach with a classic PID controller.",

author = "Arjen Hommersom and Lucas, \{Peter J.F.\} and Ren{\'e} Waarsing and Pieter Koopman",

year = "2009",

language = "English",

pages = "443--444",

journal = "Belgian/Netherlands Artificial Intelligence Conference",

issn = "1568-7805",

publisher = "University of Groningen",

note = "21st Benelux Conference on Artificial Intelligence, BNAIC 2009, BNAIC 2009 ; Conference date: 29-10-2009 Through 30-10-2009",

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T1 - Applying bayesian networks for intelligent adaptable printing systems

AU - Hommersom, Arjen

AU - Lucas, Peter J.F.

AU - Waarsing, René

AU - Koopman, Pieter

N1 - Conference code: 21

PY - 2009

Y1 - 2009

N2 - Adaptable systems are difficult to control as they have to dynamically adjust themselves at runtime to a changing environment, which is usually uncertain. Moreover, there is uncertainty about the underlying physical model of the system, which poses a problem for modelling the system's behaviour. In this paper, we propose to model the system using a Bayesian network, that can be learned, or tuned, from data. We demonstrate the usefulness of Bayesian networks for control by a case study in the area of adaptable printing systems and compare the approach with a classic PID controller.

AB - Adaptable systems are difficult to control as they have to dynamically adjust themselves at runtime to a changing environment, which is usually uncertain. Moreover, there is uncertainty about the underlying physical model of the system, which poses a problem for modelling the system's behaviour. In this paper, we propose to model the system using a Bayesian network, that can be learned, or tuned, from data. We demonstrate the usefulness of Bayesian networks for control by a case study in the area of adaptable printing systems and compare the approach with a classic PID controller.

UR - https://www.scopus.com/pages/publications/84873831667

M3 - Conference article

AN - SCOPUS:84873831667

SN - 1568-7805

SP - 443

EP - 444

JO - Belgian/Netherlands Artificial Intelligence Conference

JF - Belgian/Netherlands Artificial Intelligence Conference

T2 - 21st Benelux Conference on Artificial Intelligence, BNAIC 2009

Y2 - 29 October 2009 through 30 October 2009

ER -

Applying bayesian networks for intelligent adaptable printing systems

Abstract

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Applying bayesian networks for intelligent adaptable printing systems

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