23,485 research outputs found

    Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

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    Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun

    Agent Assistance: From Problem Solving to Music Teaching

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    We report on our research on agents that act and behave in a web learning environment. This research is part of a general approach to agents acting and behaving in virtual environments where they are involved in providing information, performing transactions, demonstrating products and, more generally, assisting users or visitors of the web environment in doing what they want or have been asked to do. While initially we hardly provided our agents with 'teaching knowledge', we now are in the process of making such knowledge explicit, especially in models that take into account that assisting and teaching takes place in a visualized and information-rich environment. Our main (embodied) tutor-agent is called Jacob; it knows about the Towers of Hanoi, a well-known problem that is offered to CS students to learn about recursion. Other agents we are working on assist a visitor in navigating in a virtual world or help the visitor in getting information. We are now designing a music teacher - using knowledge of software engineering and how to design multi-modal interactions, from previous projects

    A Knowledge-Grounded Multimodal Search-Based Conversational Agent

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    Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database. We address this new challenge by learning a neural response generation system from the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input. Our model substantially outperforms strong baselines in terms of text-based similarity measures (over 9 BLEU points, 3 of which are solely due to the use of additional information from the KB

    Reference Resolution in Multi-modal Interaction: Position paper

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    In this position paper we present our research on multimodal interaction in and with virtual environments. The aim of this presentation is to emphasize the necessity to spend more research on reference resolution in multimodal contexts. In multi-modal interaction the human conversational partner can apply more than one modality in conveying his or her message to the environment in which a computer detects and interprets signals from different modalities. We show some naturally arising problems and how they are treated for different contexts. No generally applicable solutions are given
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