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Steven R. Haynes
Researcher at Pennsylvania State University
Publications - 45
Citations - 547
Steven R. Haynes is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Soar & Information system. The author has an hindex of 12, co-authored 45 publications receiving 527 citations. Previous affiliations of Steven R. Haynes include London School of Economics and Political Science & Penn State College of Information Sciences and Technology.
Papers
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Journal ArticleDOI
Emergency Management Planning as Collaborative Community Work
TL;DR: In this paper, a case study of one community's emergency planning activities is presented, where five aspects of community preparedness are discussed: collaborative efforts, local area details, local culture, geographic information, and emergency plans.
Journal ArticleDOI
Designs for explaining intelligent agents
TL;DR: Designs that can be reused to create intelligent agents capable of explaining themselves are described and inscribe lessons learned from prior research and provide guidance for incorporating explanation facilities into intelligent systems.
Proceedings ArticleDOI
Situating evaluation in scenarios of use
TL;DR: The results suggest that scenario-based evaluation is effective in helping to focus evaluation efforts and in identifying the range of technical, human, organizational and other contextual factors that impact system success.
Dissertation
Explanation in information systems : a design rationale approach
TL;DR: This study proposes a model of IS explanation structure and content derived from formal theories of explanation with a method for obtaining this content based on design rationale and investigates the relationship between the information systems development context, and the context in which such systems are used.
High-level Behavior Representation Languages Revisited
Frank E. Ritter,Steven R. Haynes,Mark A. Cohen,Andrew Howes,Bonnie E. John,Brad Best,Christian Lebiere,Randolph M. Jones,Jacob Crossman,Richard L. Lewis +9 more
TL;DR: This symposium will identify lessons for the development of high level behavior representation languages as well as for their users by identifying generalities and common lessons.