The <i>Castalia</i> mission to Main Belt Comet 133P/Elst-Pizarro

We describe Castalia, a proposed mission to rendezvous with a Main Belt Comet (MBC), 133P/Elst-Pizarro. MBCs are a recently discovered population of apparently icy bodies within the main asteroid belt between Mars and Jupiter, which may represent the remnants of the population which supplied the early Earth with water. Castalia will perform the first exploration of this population by characterising 133P in detail, solving the puzzle of the MBC’s activity, and making the first in situ measurements of water in the asteroid belt. In many ways a successor to ESA’s highly successful Rosetta mission, Castalia will allow direct comparison between very different classes of comet, including measuring critical isotope ratios, plasma and dust properties. It will also feature the first radar system to visit a minor body, mapping the ice in the interior. Castalia was proposed, in slightly different versions, to the ESA M4 and M5 calls within the Cosmic Vision programme. We describe the science motivation for the mission, the measurements required to achieve the scientific goals, and the proposed instrument payload and spacecraft to achieve these

The Castalia mission to Main Belt Comet 133P/Elst-Pizarro

We describe Castalia, a proposed mission to rendezvous with a Main Belt Comet (MBC), 133P/Elst-Pizarro. MBCs are a recently discovered population of apparently icy bodies within the main asteroid belt between Mars and Jupiter, which may represent the remnants of the population which supplied the early Earth with water. Castalia will perform the first exploration of this population by characterising 133P in detail, solving the puzzle of the MBC's activity, and making the first in situ measurements of water in the asteroid belt. In many ways a successor to ESA's highly successful Rosetta mission, Castalia will allow direct comparison between very different classes of comet, including measuring critical isotope ratios, plasma and dust properties. It will also feature the first radar system to visit a minor body, mapping the ice in the interior. Castalia was proposed, in slightly different versions, to the ESA M4 and M5 calls within the Cosmic Vision programme. We describe the science motivation for the mission, the measurements required to achieve the scientific goals, and the proposed instrument payload and spacecraft to achieve these

Culturally adaptive mobile agent dialogue to communicate with people in crisis recovery

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

A hybrid approach to decision making and information fusion: Combining humans and artificial agents

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 visual interface for augmented human olfactory perception in the context of monitoring air quality. - Issue 1.2.0

This report presents the experiments that were carried out to investigate ways in which an intelligent adaptive interface could support inhabitants in providing accurate smell descriptions. We investigated the effect of multi-modal odor cues on human smell identification performance to inform the development of an adaptive interface for a mobile application. This involved a data elicitation study (N=429) to collect people’s olfactory associations when exposed to nine sample odors. Based on these associations, we then developed a multimodal interface that offered textual, image or combined cues to augment subjects’ odor perception, and 190 new subjects used the interface to identify odors. We found that participants’ smell identification performance increased when the interface offered visual (image and/or text) cues for odor identification. Furthermore, participants experienced the combination of visual and textual cues as most useful and enjoyable. The results of this experiment show that human smell perception can be successfully enhanced with the help of an adaptive odor cue interface. We have used the results of this study to develop a first prototype of an intelligent interface that automatically generates cues to assist human smell identification. This prototype is based on causal models (Bayesian Networks). We extracted these observation models for a few relevant chemicals. Due to the lack of data, all types of chemicals could not be covered. Nevertheless, we have shown that construction of models supporting detection and localization using human reports is possible