Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Jeffrey M. Usher

dblp:15/2048 · DBLP profile ↗
← Back
10ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5Human-computer interaction and ubiquitous computing · 5Applied, interdisciplinary, general and emerging computing · 3Graphics, computer vision, multimedia, augmented reality and games · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Knowledge representation and reasoning · 58% Vision and language · 35% Image recognition and object detection · 7%
Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%
Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
qualitative reasoning
0.112008
CogSketch · AAAI 2008
Computer vision › Vision and language
sketch understanding
0.112008
CogSketch · AAAI 2008
Knowledge, reasoning and agents › Knowledge representation and reasoning
analogical reasoning
0.112005
Analogical Learning of Visual/Conceptual Relationships in Sketches · AAAI 2005
Geometric modeling and processing
sketch understanding
0.112005
Analogical Learning of Visual/Conceptual Relationships in Sketches · AAAI 2005
Learning and educational technologies › multimedia learning
sketch-based learning
0.012008
CogSketch · AAAI 2008
Computer vision › Image recognition and object detection
visual concept learning
0.012005
Analogical Learning of Visual/Conceptual Relationships in Sketches · AAAI 2005

Methods — techniques the papers use, named apart from their topics

qualitative representation · 0.1analogy · 0.1
YearPublicationVenuePosition
2010 Sketch Worksheets: A Sketch-Based Educational Software System
abstract
Intelligent tutoring systems and learning environments can provide important benefits for education, but few have been developed for heavily spatial domains. One bottleneck has been the lack of rich models of visual and conceptual processing in sketch understanding, so that what students draw can be interpreted in a human-like way. This paper describes Sketch Worksheets, a form of sketch-based educational software that mimics aspects of pencil and paper worksheets commonly found in classrooms, but provides on-the-spot feedback and support for richer off-line assessments. The basic architecture of sketch worksheets is described, including an authoring environment that allows non-developers to create them and a coach that uses analogy to compare student and instructor sketches as a means to provide feedback. A pilot experiment where sketch worksheets were used successfully in a college geoscience class in Fall 2009 is summarized to show the potential of the idea.
Panrong Yin, Kenneth D. Forbus, Jeffrey M. Usher, Bradley Sageman, Benjamin D. Jee
IAAI3
2008 CogSketch
Kenneth D. Forbus, Andrew M. Lovett, Kate Lockwood, Jon Wetzel, Camillia Matuk, Benjamin D. Jee, Jeffrey M. Usher
AAAI7
2005 Analogical Learning of Visual/Conceptual Relationships in Sketches
Kenneth D. Forbus, Jeffrey M. Usher, Emmett Tomai
AAAI2
2003 A Knowledge Acquisition Tool for Course of Action Analysis
Kim Barker, Jim Blythe, Gary C. Borchardt, Vinay K. Chaudhri, Peter Clark, Paul R. Cohen, Julie Fitzgerald, Kenneth D. Forbus, Yolanda Gil, Boris Katz, Jihie Kim, Gary W. King, Sunil Mishra, Clayton T. Morrison, Kenneth S. Murray, Charley Otstott, Bruce W. Porter, Robert Schrag, Tomás E. Uribe, Jeffrey M. Usher, Peter Z. Yeh
IAAI20
2003 Qualitative Spatial Reasoning about Sketch Maps
Kenneth D. Forbus, Jeffrey M. Usher, Vernell Chapman
IAAI2
2003 Sketching for military courses of action diagrams
abstract
A serious barrier to the digitalization of the US military is that commanders find traditional mouse/menu, CAD-style interfaces unnatural. Military commanders develop and communicate battle plans by sketching courses of action (COAs). This paper describes nuSketch Battlespace, the latest version in an evolving line of sketching interfaces that commanders find natural, yet supports significant increased automation. We describe techniques that should be applicable to any specialized sketching domain: glyph bars and compositional symbols to tractably handle the large number of entities that military domains use, specialized glyph types and gestures to keep drawing tractable and natural, qualitative spatial reasoning to provide sketch-based visual reasoning, and comic graphs to describe multiple states and plans. Experiments, both completed and in progress, are described to provide evidence as to the utility of the system.
Kenneth D. Forbus, Jeffrey M. Usher, Vernell Chapman
IUI2
2003 nuSketch battlespace: a demonstration
abstract
Sketching provides a natural means of interaction for many spatially-oriented tasks. One task where sketching is used extensively is when military planners are formulating battle plans, called Courses of Action (COAs). This paper describes a system we have built, nuSketch Battlespace (nSB), which provides a sketching interface for creating COAs. The system is described in the paper "Sketching for Military Courses of Action" in these proceedings. The demonstration will highlight:
Kenneth D. Forbus, Jeffrey M. Usher, Vernell Chapman
IUI2
2002 Sketching for knowledge capture: a progress report
abstract
Many concepts and situations are best explained by sketching. This paper describes our work on sKEA, the sketching Knowledge Entry Associate, a system designed for knowledge capture via sketching. We discuss the key ideas of sKEA: blob semantics for glyphs to sidestep recognition for visual symbols, qualitative spatial reasoning to provide richer visual and conceptual understanding of what is being communicated, arrows to express domain relationships, layers to express within-sketch segmentation (including a meta-layer to express subsketch relationships themselves via sketching), and analogical comparison to explore similarities and differences between sketched concepts. Experiences with sKEA to date and future plans are also discussed.
Kenneth D. Forbus, Jeffrey M. Usher
IUI2
2002 Sketching for knowledge capture: a demonstration
abstract
Many concepts and situations are best explained by sketching. This demonstration will show the key ideas underlying sKEA, the sketching knowledge entry associate, a system we have built for knowledge capture via sketching. In particular, we will demonstrateHow glyph bars and blob semantics are used to sidestep the need for recognition of visual symbols. The use of qualitative spatial reasoning to provide richer visual and conceptual understanding of what is being communicatedHow arrows are used to express domain relationshipsThe use of layers to express within-sketch segmentation, including a meta-layer to express subsketch relationships themselves via sketchingUsing analogical comparison to explore similarities and differences between sketched concepts.
Kenneth D. Forbus, Jeffrey M. Usher
IUI2
2001 Towards a computational model of sketching
abstract
Sketching is a powerful means of interpersonal communication. While many useful multimodal systems have been created, current systems are far from achieving human-like participation in sketching. A computational model of sketching would help characterize these differences and help us better understand how to overcome them. This paper is a first step towards such a model. We start with an example of a sketching system(nuSketch COA Creator)designed to aid military planners, to provide context and a source of examples. We then describe four dimensions of sketching,visual understanding, conceptual understanding, language understanding,anddrawing,that can be used to characterize the competence of existing systems and identify open problems. The issues involved will be illustrated by examples from our experience with nuSketch. Three research challenges are posed, to serve as milestones towards a computational model of sketching that can explain and replicate human abilities in this area.
Kenneth D. Forbus, Ronald W. Ferguson, Jeffrey M. Usher
IUI3