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This paper presents a study where Augmented Reality (AR) technology has been used as a tool for supporting collaboration between the rescue services, the police and military personnel in a crisis management scenario. There are few studies on how AR systems should be designed to improve cooperation between actors from different organizations while at the same time support individual needs. In the present study an AR system was utilized for supporting joint planning tasks by providing organisation-specific views of a shared working. The study involved a simulated emergency event conducted in close to real settings with representatives from the organisations for which the system is developed. As a baseline, a series of trials without the AR system was carried out. Results show that the users were positive towards the AR system, and would like to use it in real work. They also experience some performance benefits of using the AR system compared to their traditional tools. Finally, the problem of designing for collaborative work as well as the benefits of using an iterative design processes is discussed.

Document classification using automated linguistic analysis and machine learning (ML) has been shown to be a viable road forward for readability assessment. The best models can be trained to decide if a text is easy to read or not with very high accuracy, e.g. a model using 117 parameters from shallow, lexical, morphological and syntactic analyses achieves 98,9% accuracy.In this paper we compare models created by parameter optimization over subsets of that total model to find out to which extent different high-performing models tend to consist of the same parameters and if it is possible to find models that only use features not requiring parsing. We used a genetic algorithm to systematically optimize parameter sets of fixed sizes using accuracy of a Support Vector Machine classifier as fitness function.Our results show that it is possible to find models almost as good as the currently best models while omitting parsing based features.

Natural language interfaces require dialogue models that allow for robust, habitable and efficient interaction. This paper presents such a model for dialogue management for natural language interfaces. The model is based on empirical studies of human computer interaction in various simple service applications. It is shown that for applications belonging to this class the dialogue can be handled using fairly simple means. The interaction can be modeled in a dialogue grammar with information on the functional role of an utterance as conveyed in the linguistic structure. Focusing is handled using dialogue objects recorded in a dialogue tree representing the constituents of the dialogue. The dialogue objects in the dialogue tree can be accessed by the various modules for interpretation, generation and background system access. Focused entities are modeled in entities pertaining to objects or sets of objects, and related domain concept information; properties of the domain objects. A simple copying principle, where a new dialogue object's focal parameters are instantiated with information from the preceding dialogue object, accounts for most context dependent utterances. The action to be carried out by the interface is determined on the basis of how the objects and related properties are specified. This in turn depends on information presented in the user utterance, context information from the dialogue tree and information in the domain model. The use of dialogue objects facilitates customization to the sublanguage utilized in a specific application. The framework has successfully been applied to various background systems and interaction modalities. In the paper results from the customization of the dialogue manager to three typed interaction applications are presented together with results from applying the model to two applications utilizing spoken interaction.

We present the results from a series of experiments aimed at uncovering the discourse structure of man-machine communication in natural language (Wizard of Oz experiments). The results suggest the existence of different classes of dialogue situations, requiring computational discourse representations of various complexity. Important factors seem to be the number of different permissible tasks in the system and to what extent the system takes initiative in the dialogue. We also analyse indexical expressions and especially the use of pronouns, and suggest a psychological explanation of their restricted occurrence in these types of dialogues.-291 -