活动简介
The purpose of the workshop is to exchange ideas and address a set of questions to help participants gain new insights into the design of systems that help users (typically in groups or communities) solve and/or understand complex problems, such as instances of global-challenges and wicked problems. Such problems include aspects of climate change, drug-resistant diseases, nuclear proliferation, fake news, global health, clean water, and urban homelessness. The increasing sophistication of software systems also makes complex problem solving increasingly important in software engineering. Recent technology developments suggest new approaches to integrated systems that combine human-centered computing, crowdsourcing, artificial intelligence and machine learning, sensor networks, and computer-supported collaborative work.
组委会
- Margaret Burnett, Oregon State Univ., USA
- Aritra Dasgupta, Pacific Northwest National Laboratories, USA
- Enamul Hoque, Stanford Univ., USA
- Andy Ko, Univ. of Wash., USA
- Denis Lalanne, Univ. of Fribourg, Switzerland
- Brad Myers, Carnegie-Mellon Univ., USA
- Barry O'Sullivan, Cork Constraints Computation Center, Ireland
- Steven Rick, Univ. Calif. San Diego, USA
- Ali Sarvghad, Univ. Calif. San Diego, USA
- Cliff Shaffer, Virginia Polytech. Univ., USA
- Mark Whiting, Stanford Univ., USA
- Amy Zhang, Massachusetts Institute of Tech., USA
征稿信息
重要日期
Through paper presentations, a keynote lecture, and discussions, the workshop will address the following questions:
- What possible new forms of human problem-solving experiences should be supported by new technological tools?
- What social structures and workflows may need to be supported?
- How should students and professionals be educated and trained in order to be able to function most productively in these new human-technical environments for problem solving?
- What are potential roles for crowdsourcing, artificial intelligence, machine learning, intelligent tutoring, and the Internet of Things in these systems?
- How should systems be designed to make biases in machine learning processes more visible and how can these systems allow humans to compensate for those biases?
- What might existing and possible future theories of problem solving contribute to the design of these systems?
- What provisions should be made to promote diverse community engagement in problem solving, designing for inclusion across identities as they may relate to aspects such as socio-economic status, gender, culture, etc?
- What should a research agenda in this field consist of?
作者指南
We invite five kinds of paper submissions, and we invite all to take part in discussions with or without papers. The paper types are as follows:
- Long research papers, or research survey papers, up to 8 pp.
- Short research papers, up to 4 pp.
- Work-in-progress papers, up to 4 pp.
- Position papers, up to 4 pp.
- Posters, with 2-page proceedings summary papers.
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重要日期
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10月01日
2018
会议日期
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07月06日 2018
摘要截稿日期
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07月13日 2018
初稿截稿日期
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08月01日 2018
初稿录用通知日期
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10月01日 2018
注册截止日期
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