Workshops & Tutorials will be on March 17, 2020.
An adaptive user interface (AUI) can alter its appearance, content, or behaviour based on the user and their context. To maximise the effectiveness and utility of adaptations, it is necessary to accurately predict desirable qualities of an interface for a given circumstance. It is also beneficial to plan and implement these adaptations such that they are not counter-productive. This workshop aims at discussing, exploring, and prototyping artificial intelligence (AI) methods for designing and implementing AUIs. By implementing these methods within interactive systems, AUIs could successfully improve key usability aspects such as performance and aesthetics.
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In the field of Human Robot Interaction (HRI) there is a consensus about the design and implementation of robotic systems that should be able to adapt their behaviour to the user. The robot should adapt to user’s emotions, personalities, etc. and it should also have memory of past interactions with the use. The aim of this workshop is to bring together researchers and practitioners who are working on various aspects of social robotics and adaptive interaction. The expected result of the workshop is a multidisciplinary research agenda that will inform future research directions and, hopefully, forge some research collaborations.
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This multidisciplinary workshop aims to tackle the significant gaps in theoretical frameworks, methodological approaches, and exploration of new paradigms within the research and design of Conversational User Interfaces (CUIs). Such gaps include: the lack of validated design guidelines to help us improve the usability of CUIs; handling the variability in speech, language, and conversation that still pose problems in both interface design and speech engineering; error-recovery strategies that often lead to degraded user experience; understanding how user behaviours and choices may apply to specific CUI interactions; and issues of ethics and privacy.
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Smart systems that apply complex reasoning to make decisions and plan behavior, such as decision support systems and personalized recommendations, are difficult for users to understand. Algorithms allow the exploitation of rich and varied data sources, in order to support human decision-making and/or taking direct actions; however, there are increasing concerns surrounding their transparency and accountability, as these processes are typically opaque to the user - e.g., because they are too technically complex to be explained or are protected trade secrets. This workshop will provide a venue for exploring issues that arise in designing, developing and evaluating intelligent user interfaces that provide system transparency or explanations of their behavior. In addition, our goal is to focus on approaches to mitigating algorithmic biases that can be applied by researchers, even without access to a given system’s inter-workings, such as awareness, data provenance, and validation.
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Recent advances in generative modeling through deep learning approaches such as generative adversarial networks (GANs), variational autoencoders (VAEs), and language models will enable new kinds of user experiences around content creation, giving us “creative superpowers” and move us toward co-creation. The goal of this workshop is to bring together researchers and practitioners from both fields HCI and AI to explore and better understand both the opportunities and challenges of generative modelling from an HCI perspective. We envision that the user experience of creating both physical and digital artifacts will become a partnership of humans and AI: Humans will take the role of specification, goal setting, steering, high-level creativity, curation, and governance. AI will augment human abilities through inspiration, low level creativity and detail work, and the ability to test ideas at scale.
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HUMANIZE aims to investigate how intelligent, adaptive systems can benefit from combining quantitative, data-driven approaches with qualitative, theory-driven approaches. We in particular invite work from researchers that incorporate features grounded in psychological theory (e.g. personality, cognitive styles etc.) into the predictive models underlying their adaptive/intelligent systems (e.g. recommender systems, website morphing, etc.). Apart from research investigating how this approach can improve these systems, we are interested in research towards the potential of this approach in improving explainability, fairness and transparency and reducing bias in data or output of intelligent systems.
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Given the increasing adoption of personal health services and devices, research on smart personal health interfaces is a hot topic for the communities of AI and human-computer interaction. In order to enhance acceptance and effectiveness of personal health systems, it is necessary to devise novel methods to establish interaction among users and their personal devices in an advanced and intuitive way. SmartPHIL aims to bring together researchers, industry, and the community interested in next-generation personalized health services exploiting AI for leveraging natural interaction. We expect to receive both mature contributions and early results on these topics, illustrating innovative research directions.
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Conversational agent systems present an extremely rich and challenging research space for many topics of user awareness and adaptation, such as user profiles, contexts, personalities, emotions, social dynamics, conversational styles, etc. The user2agent workshop aims to bring together researchers who are interested in these topics from different communities, including user modeling, HCI, NLP and ML. Through a focused and open exchange of idea, the 2nd workshop on user-aware conversational agents will discuss not only advance the state of the art, but also analyze and understand the current state of the art, and point to future research directions.
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This tutorial introduces practical Bayesian approaches to interaction and design. Bayesian methods offer a powerful approach for thinking about and implementing interactive systems that can deal with uncertainty and noise. This course introduces the theory and practice of computational Bayesian interaction, covering inference of intention and design of interface features. Participants will explore interactive examples in the tutorial, which is built around hands-on Python programming with modern computational tools. This is interleaved with presentations covering theory and more in-depth examples of problems of wide interest in human-computer interaction.
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Want to learn what is required to make a CUI feel natural and make it successful with users? And what is so special about language as an interaction modality? Learn linguistic foundations behind a good conversation and practice design and evaluation of chatbots. Through a combination of business, technology and user perspectives, our tutorial addresses academics and practitioners from technology and UX. As this is a multidisciplinary tutorial (HCI, language technology and study), it is suitable for beginners as well as advanced participants in the field of CUI and will help people to bridge the gaps between the disciplines.
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