liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Message classification as a basis for studying command and control communication: an evaluation of machine learning approaches
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: Journal of Intelligent Information Systems, ISSN 0925-9902, E-ISSN 1573-7675, Vol. 38, no 2, 299-320 p.Article in journal (Refereed) Published
Abstract [en]

In military command and control, success relies on being able to perform key functions such as communicating intent. Most staff functions are carried out using standard means of text communication. Exactly how members of staff perform their duties, who they communicate with and how, and how they could perform better, is an area of active research. In command and control research, there is not yet a single model which explains all actions undertaken by members of staff well enough to prescribe a set of procedures for how to perform functions in command and control. In this context, we have studied whether automated classification approaches can be applied to textual communication to assist researchers who study command teams and analyze their actions. Specifically, we report the results from evaluating machine leaning with respect to two metrics of classification performance: (1) the precision of finding a known transition between two activities in a work process, and (2) the precision of classifying messages similarly to human researchers that search for critical episodes in a workflow. The results indicate that classification based on text only provides higher precision results with respect to both metrics when compared to other machine learning approaches, and that the precision of classifying messages using text-based classification in already classified datasets was approximately 50%. We present the implications that these results have for the design of support systems based on machine learning, and outline how to practically use text classification for analyzing team communications by demonstrating a specific prototype support tool for workflow analysis.

Place, publisher, year, edition, pages
Berlin: Springer , 2012. Vol. 38, no 2, 299-320 p.
Keyword [en]
Command and control – Classification, Exploratory sequential data analysis, Workflow mining, Random indexing, Text clustering
National Category
Computer Science
URN: urn:nbn:se:liu:diva-67227DOI: 10.1007/s10844-011-0156-5ISI: 000302240800001OAI: diva2:408434

funding agencies|Swedish National Defense College||

Available from: 2011-04-06 Created: 2011-04-04 Last updated: 2012-05-03Bibliographically 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

Open Access in DiVA

fulltext(683 kB)604 downloads
File information
File name FULLTEXT02.pdfFile size 683 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Leifler, OlaEriksson, Henrik
By organisation
MDALAB - Human Computer InterfacesThe Institute of Technology
In the same journal
Journal of Intelligent Information Systems
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 604 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 80 hits
ReferencesLink to record
Permanent link

Direct link