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Automated text-based analysis for decision-making research
Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
2012 (English)In: Cognition, Technology & Work, ISSN 1435-5558, E-ISSN 1435-5566, Vol. 14, no 2, 129-142 p.Article in journal (Refereed) Published
Abstract [en]

We present results from a study on constructing and evaluating a support tool for the extraction of patterns in distributed decision -making processes, based on design criteria elicited from a study on the work process involved in studying such decision-making. Specifically, we devised and evaluated an analysis tool for C2 researchers who study simulated decision-making scenarios for command teams. The analysis tool used text clustering as an underlying pattern extraction technique and was evaluated together with C2 researchers in a workshop to establish whether the design criteria were valid and the approach taken with the analysis tool was sound. Design criteria elicited from an earlier study with researchers (open-endedness and transparency) were highly consistent with the results from the workshop. Specifically, evaluation results indicate that successful deployment of advanced analysis tools requires that tools can treat multiple data sources and offer rich opportunities for manipulation and interaction (open-endedness) and careful design of visual presentations and explanations of the techniques used (transparency). Finally, the results point to the high relevance and promise of using text clustering as a support for analysis of C2 data.

Place, publisher, year, edition, pages
Springer London, 2012. Vol. 14, no 2, 129-142 p.
Keyword [en]
Command and control, Text analysis, Exploratory sequential data analysis, Text clustering
National Category
Computer Science
URN: urn:nbn:se:liu:diva-67229DOI: 10.1007/s10111-010-0170-3ISI: 000310239500003OAI: diva2:408436

The original publication is available at Ola Leifler and Henrik Eriksson, Text-based Analysis for Command and Control Researchers: The Workflow Visualizer Approach, 2011, Cognition, Technology & Work. Copyright: Springer Science Business Media

Available from: 2011-04-06 Created: 2011-04-04 Last updated: 2014-01-13Bibliographically approved
In thesis
1. Affordances and Constraints of Intelligent Decision Support for Military Command and Control: Three Case Studies of Support Systems
Open this publication in new window or tab >>Affordances and Constraints of Intelligent Decision Support for Military Command and Control: Three Case Studies of Support Systems
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Möjligheter och begränsningar med intelligent  beslutsstöd i militär ledning : Tre fallstudier av teknikstöd
Abstract [en]

Researchers in military command and control (C2) have for several decades sought to help commanders by introducing automated, intelligent decision support systems. These systems are still not widely used, however, and some researchers argue that this may be due to those problems that are inherent in the relationship between the affordances of technology and the requirements by the specific contexts of work in military C2. In this thesis, we study some specific properties of three support techniques for analyzing and automating aspects of C2 scenarios that are relevant for the contexts of work in which they can be used.

The research questions we address concern (1) which affordances and constraints of these technologies are of most relevance to C2, and (2) how these affordances and limitations can be managed to improve the utility of intelligent decision support systems in C2. The thesis comprises three case studies of C2 scenarios where intelligent support systems have been devised for each scenario.

The first study considered two military planning scenarios: planning for medical evacuations and similar tactical operations. In the study, we argue that the plan production capabilities of automated planners may be of less use than their constraint management facilities. ComPlan, which was the main technical system studied in the first case study, consisted of a highly configurable, collaborative, constraint-management framework for planning in which constraints could be used either to enforce relationships or notify users of their validity during planning. As a partial result of the first study, we proposed three tentative design criteria for intelligent decision support: transparency, graceful regulation and event-based feedback.

The second study was of information management during planning at the operational level, where we used a C2 training scenario from the Swedish Armed Forces and the documents produced during the scenario as a basis for studying properties of Semantic Desktops as intelligent decision support. In the study, we argue that (1) due to the simultaneous use of both documents and specialized systems, it is imperative that commanders can manage information from heterogeneous sources consistently, and (2) in the context of a structurally rich domain such as C2, documents can contain enough information about domain-specific concepts that occur in several applications to allow them to be automatically extracted from documents and managed in a unified manner. As a result of our second study, we present a model for extending a general semantic desktop ontology with domain-specific concepts and mechanisms for extracting and managing semantic objects from plan documents. Our model adheres to the design criteria from the first case study.

The third study investigated machine learning techniques in general and text clustering in particular, to support researchers who study team behavior and performance in C2. In this study, we used material from several C2 scenarios which had been studied previously. We interviewed the participating researchers about their work profiles, evaluated machine learning approaches for the purpose of supporting their work and devised a support system based on the results of our evaluations. In the study, we report on empirical results regarding the precision possible to achieve when automatically classifying messages in C2 workflows and present some ramifications of these results on the design of support tools for communication analysis. Finally, we report how the prototype support system for clustering messages in C2 communications was conceived by the users, the utility of the design criteria from case study 1 when applied to communication analysis, and the possibilities for using text clustering as a concrete support tool in communication analysis.

In conclusion, we discuss how the affordances and constraints of intelligent decision support systems for C2 relate to our design criteria, and how the characteristics of each work situation demand new adaptations of the way in which intelligent support systems are used.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 154 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1381
Decision Support, planning, machine learning, information management, Command and Control
National Category
Computer Science
urn:nbn:se:liu:diva-67630 (URN)978-91-7393-133-5 (ISBN)
Public defence
2011-06-17, Key 1, Hus Key, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Available from: 2011-05-26 Created: 2011-04-20 Last updated: 2012-03-27Bibliographically approved

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