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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.

A common task for spoken dialog systems (SDS) is to help users select a suitable option (e.g., flight, hotel, and restaurant) from the set of options available. As the number of options increases, the system must have strategies for generating summaries that enable the user to browse the option space efficiently and successfully. In the user-model based summarize and refine approach (UMSR, Demberg and Moore, 2006), options are clustered to maximize utility with respect to a user model, and linguistic devices such as discourse cues and adverbials are used to highlight the trade-offs among the presented items. In a Wizard-of-Oz experiment, we show that the UMSR approach leads to improvements in task success, efficiency, and user satisfaction compared to an approach that clusters the available options to maximize coverage of the domain (Polifroni et al., 2003). In both a laboratory experiment and a web-based experimental paradigm employing the Amazon Mechanical Turk platform, we show that the discourse cues in UMSR summaries help users compare different options and choose between options, even though they do not improve verbatim recall. This effect was observed for both written and spoken stimuli.

This paper reports the application of results from human- social agent interaction experiments to inform the design of a social robot to monitor levels of pollutive gasses in the air. Next to licensed environmental agents and immobile chemical sensors, mobile technologies such as robotic agents are needed to collect complaints and smell descriptions from humans in urban industrial areas. These robots will interact with members of the public and ensure responsiveness and accuracy of responses. For robots to be accepted as representative environmental monitoring agents and for people to comply to robot instructions in the case of a calamity, social skills will be important. In this paper we will describe the intelligent environment the environmental robot is part of and discuss preliminary work on the effects of robot empathic and touch behaviors on human responses to robots. These and future findings will inform the design of social monitoring robot behaviors in public settings.

Being interrupted by notifications and reminders is common while working. In this study we consider whether system politeness reduces (negative) effects of being interrupted by system requests. We carried out a 2 (polite vs. neutral system request) x 2 (high vs. low mental load) between-participants experiment. We measured annoyance, frustration and mental effort. Our results suggest that social strategies can mitigate some of the negative effects, but that this depends on the difficulty of the task. We discuss the implications of these results for the design of interruptive system messages and for further research into social computing.

Environmental monitoring and emergency response projects in urban-industrial areas increasingly rely on efficient collaboration between experts in control rooms and at incident locations, and citizens who live or work in the area. In the video accompanying this abstract we present a system that uses distributed sensor technology, Bayesian decision tools, and advanced map-based interfaces to facilitate collaboration between environmental experts and the public for environmental monitoring and early detection of chemical incidents.

We present ongoing research concerning the interaction between users and environmental agencies through autonomous mobile agents in the environmental monitoring domain. The overarching EU FP7 project DIADEM, concerns the development of a system that detects potentially hazardous situations in populated areas using input from both a distributed sensor network and humans through their mobile devices. We propose a model of interaction with a system where concerned citizens communicate with a social virtual agent through their mobile phone to inform the environmental monitoring agency about unusual smells. In case of an emergency, people will receive instructions or directions for evacuation from the agent. In this paper, we review relevant literature and describe the development of a dynamic dialogue agent that supports international collaboration by adapting its social interaction to the cultural background of the humans it interacts with.

In this paper, we present ongoing research concerning the interaction between users and autonomous mobile agents in the environmental monitoring domain. The overarching project, DIADEM, deals with developing a system that detects potentially hazardous situations in populated industrial areas using input from both a distributed sensor network and humans through mobile devices. We propose a model of interaction with the gas detection system where concerned citizens communicate with a mobile agent to inform the gas monitoring system about unusual smells via their mobile phones. Next, we present a preliminary user requirements analysis based on 40 phone calls from members of the public to an environmental monitoring agency. Finally, we introduce measures to study the delicate long-term social relationship between users and the gas monitoring system.

In this paper we examine the effect of linguistic devices on recall and comprehension in information presentation using both recall and eye-tracking data. In addition, the results were validated via an experiment using Amazon's Mechanical Turk micro-task environment.

A 2x2 enhanced Wizard-of-Oz experiment (N = 32) was conducted to compare two different approaches to presenting information to drivers in easy and difficult driving conditions. Data of driving safety, evaluation of the spoken dialogue system, and perception of self were analyzed. Results show that the user-modeled summarize-and-refine (UMSR) approach led to more efficient information retrieval than did the summarize-and-refine (SR) approach. However, depending on driving condition, higher efficiency did not always translate into pleasant subjective experience. Implications for usability testing and interface design were presented, followed by discussions of future research directions.

This paper presents results of a Wizard-of-Oz (WoZ) study examining the effect of two different information presentation methods on a secondary task, namely driving. The results demonstrate that the user-model based summarize and refine(UMSR) approach enables more efficient information retrieval in comparison to the data-driven summarize and refine (SR) approach, and does not negatively affect driving performance.