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dc.contributor.authorMaulsby, Daviden_US
dc.contributor.authorWitten, Ian H.en_US
dc.date.accessioned2008-03-19T04:58:06Z
dc.date.available2007-05-15en_US
dc.date.available2008-03-19T04:58:06Z
dc.date.issued1997-11-01en_US
dc.identifier.citationMaulsby, D. & Witten, I. H. (1997). Teaching agents to learn: from user study to implementation. IEEE Computer, 30(11),36-43en_US
dc.identifier.urihttps://hdl.handle.net/10289/50
dc.description.abstractGraphical user interfaces have helped center computer use on viewing and editing, rather than on programming. Yet the need for end-user programming continues to grow. Software developers have responded to the demand with a barrage of customizable applications and operating systems. But the learning curve associated with a high level of customizability-even in GUI-based operating systems-often prevents users from easily modifying their software. Ironically, the question has become, "What is the easiest way for end users to program?" Perhaps the best way to customize a program, given current interface and software design, is for users to annotate tasks-verbally or via the keyboard-as they are executing them. Experiments have shown that users can "teach" a computer most easily by demonstrating a desired behavior. But the teaching approach raises new questions about how the system, as a learning machine, will correlate, generalize, and disambiguate a user's instructions. To understand how best to create a system that can learn, the authors conducted an experiment in which users attempt to train an intelligent agent to edit a bibliography. Armed with the results of these experiments, the authors implemented an interactive machine learning system, which they call Configurable Instructible Machine Architecture. Designed to acquire behavior concepts from few examples, Cima keeps users informed and allows them to influence the course of learning. Programming by demonstration reduces boring, repetitive work. Perhaps the most important lesson the authors learned is the value of involving users in the design process. By testing and critiquing their design ideas, users keep the designers focused on their objective: agents that make computer-based work more productive and more enjoyable.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEE COMPUTER SOCen_NZ
dc.relation.urihttp://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=634839&abstractAccess=no&userType=insten_US
dc.rightsThis article is available online at the webpages of IEEE Computer.en_US
dc.subjectMachine learning
dc.titleTeaching agents to learn: from user study to implementationen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1109/2.634839en_NZ
dc.relation.isPartOfComputeren_NZ
pubs.begin-page36en_NZ
pubs.elements-id39438
pubs.end-page44en_NZ
pubs.issue11en_NZ
pubs.volume30en_NZ
uow.identifier.article-no11en_NZ


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