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.