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1
Strong Artificial Intelligence and National Security: Operational and Strategic Implications
In: DTIC (2015)
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2
IMMERSE: Interactive Mentoring for Multimodal Experiences in Realistic Social Encounters
In: DTIC (2015)
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3
Mobile Active Authentication via Linguistic Modalities
In: DTIC (2015)
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4
Uncertainty Can Increase Explanatory Credibility
In: DTIC (2013)
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5
Representing and Reasoning with Functional Knowledge for Spatial Language Understanding
In: DTIC (2011)
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6
Trends in Human-Computer Interaction to Support Future Intelligence Analysis Capabilities
In: DTIC (2011)
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7
Transfer Learning in Integrated Cognitive Systems
In: DTIC (2010)
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8
Analysis of Discourse Accent and Discursive Practices I&W
In: DTIC (2010)
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9
Transformational Impact of "Digital Natives" on Cultures, Commerce and Societies
In: DTIC (2010)
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10
Plastic Machines: Behavioural Diversity and the Turing Test
Wheeler, Michael. - : Emerald, 2010
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11
Dual-Task Performance with Center-Surround Auditory-Only Displays
In: DTIC (2009)
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12
Metacognitive Awareness versus Linguistic Politeness: Expressions of Confusion in Tutorial Dialogues
In: DTIC (2009)
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13
The Use of a Context-Based Information Retrieval Technique
In: DTIC (2009)
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14
A Sensemaking Visualization Tool with Military Doctrinal Elements
In: DTIC (2008)
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15
Linking Semantic and Knowledge Representations in a Multi-Domain Dialogue System
In: DTIC (2007)
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16
Common Navy Warfighting Display Symbology Implementation Guide
In: DTIC (2007)
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17
Error Awareness and Recovery in Conversational Spoken Language Interfaces
In: DTIC (2007)
Abstract: One of the most important and persistent problems in the development of conversational spoken language interfaces is their lack of robustness when confronted with understanding-errors. Most of these errors stem from limitations in current speech recognition technology, and, as a result, appear across all domains and interaction types. There are two approaches towards increased robustness: prevent the errors from happening, or recover from them through conversation, by interacting with the users. In this dissertation we have engaged in a research program centered on the second approach. We argue that three capabilities are needed in order to seamlessly and efficiently recover from errors: (1) systems must be able to detect the errors, preferably as soon as they happen, (2) systems must be equipped with a rich repertoire of error recovery strategies that can be used to set the conversation back on track, and (3) systems must know how to choose optimally between different recovery strategies at run-time, i.e. they must have good error recovery policies. This work makes a number of contributions in each of these areas.
Keyword: *ERRORS; *MAN COMPUTER INTERFACE; *POLICIES; *RAVENCLAW FRAMEWORK; *RECOVERY; *RECOVERY STRATEGIES; *SPEECH RECOGNITION; *SPOKEN DIALOG SYSTEMS; ARCHITECTURE; CONVERSATIONS; Cybernetics; DETECTION; ERROR HANDLING; LEARNING MACHINES; MISUNDERSTANDINGS; NON-UNDERSTANDINGS; SPOKEN LANGUAGE INTERFACES; THESES; Voice Communications
URL: http://www.dtic.mil/docs/citations/ADA476845
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA476845
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18
The Error Is the Clue: Breakdown In Human-Machine Interaction
In: DTIC (2006)
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19
Virtual Rapport
In: DTIC (2006)
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20
Dealing with Out of Domain Questions in Virtual Characters
In: DTIC (2006)
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