A hybrid approach to decision making and information fusion: Combining humans and artificial agents

Frans C.A. Groen, Gregor Pavlin, Andi Winterboer, Vanessa Evers

  • 2 Citations

Abstract

This paper argues that hybrid human–agent systems can support powerful solutions to relevant problems such as Environmental Crisis management. However, it shows that such solutions require comprehensive approaches covering different aspects of data processing, model construction and the usage. In particular, the solutions (i) must be able to cope with complex correlations (as different data sources are used) and processing of large amounts of data, (ii) must be robust against modeling imperfections and (iii) human–machine interaction (HMI) approaches must facilitate human use of crisis management tools and reduce the likelihood of miscommunication. In this paper the relevant problem is an environmental protection application involving the detection and tracking of gases in case of chemical spills in an urban area. We show that a combination of Bayesian Networks, agent paradigm and systematic approaches to implementing HMI, support effective and robust solutions. To better integrate human information and demonstrate the usefulness of user generated crisis response information we developed a social media harvesting interface based on data from Twitter tweets and a visual interface to facilitate human smell classification.
Original languageUndefined
Pages (from-to)71-85
Number of pages15
JournalRobotics and autonomous systems
Volume90
Issue numberApril 2017
DOIs
StatePublished - Apr 2017

Fingerprint

Environmental management
Hazardous materials spills
Bayesian networks
Environmental protection
Defects
Gases

Keywords

  • EWI-27596
  • HMI-IA: Intelligent Agents
  • EC Grant Agreement nr.: FP7/611143
  • Human agent systems
  • IR-104413
  • Bayesian Networks
  • Gas detection
  • Information fusion
  • Environmental crisis management

Cite this

Groen, Frans C.A.; Pavlin, Gregor; Winterboer, Andi; Evers, Vanessa / A hybrid approach to decision making and information fusion: Combining humans and artificial agents.

In: Robotics and autonomous systems, Vol. 90, No. April 2017, 04.2017, p. 71-85.

Research output: Scientific - peer-reviewArticle

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title = "A hybrid approach to decision making and information fusion: Combining humans and artificial agents",
abstract = "This paper argues that hybrid human–agent systems can support powerful solutions to relevant problems such as Environmental Crisis management. However, it shows that such solutions require comprehensive approaches covering different aspects of data processing, model construction and the usage. In particular, the solutions (i) must be able to cope with complex correlations (as different data sources are used) and processing of large amounts of data, (ii) must be robust against modeling imperfections and (iii) human–machine interaction (HMI) approaches must facilitate human use of crisis management tools and reduce the likelihood of miscommunication. In this paper the relevant problem is an environmental protection application involving the detection and tracking of gases in case of chemical spills in an urban area. We show that a combination of Bayesian Networks, agent paradigm and systematic approaches to implementing HMI, support effective and robust solutions. To better integrate human information and demonstrate the usefulness of user generated crisis response information we developed a social media harvesting interface based on data from Twitter tweets and a visual interface to facilitate human smell classification.",
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A hybrid approach to decision making and information fusion: Combining humans and artificial agents. / Groen, Frans C.A.; Pavlin, Gregor; Winterboer, Andi; Evers, Vanessa.

In: Robotics and autonomous systems, Vol. 90, No. April 2017, 04.2017, p. 71-85.

Research output: Scientific - peer-reviewArticle

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AU - Winterboer,Andi

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AB - This paper argues that hybrid human–agent systems can support powerful solutions to relevant problems such as Environmental Crisis management. However, it shows that such solutions require comprehensive approaches covering different aspects of data processing, model construction and the usage. In particular, the solutions (i) must be able to cope with complex correlations (as different data sources are used) and processing of large amounts of data, (ii) must be robust against modeling imperfections and (iii) human–machine interaction (HMI) approaches must facilitate human use of crisis management tools and reduce the likelihood of miscommunication. In this paper the relevant problem is an environmental protection application involving the detection and tracking of gases in case of chemical spills in an urban area. We show that a combination of Bayesian Networks, agent paradigm and systematic approaches to implementing HMI, support effective and robust solutions. To better integrate human information and demonstrate the usefulness of user generated crisis response information we developed a social media harvesting interface based on data from Twitter tweets and a visual interface to facilitate human smell classification.

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KW - Information fusion

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