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Visualization of Decision Processes Using a Cognitive Architecture
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In: DTIC (2013)
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Abstract:
Cognitive architectures are computational theories of reasoning the human mind engages in as it processes facts and experiences. A cognitive architecture uses declarative and procedural knowledge to represent mental constructs that are involved in decision making. Employing a model of behavioral and perceptual constraints derived from a set of one or more scenarios, the architecture reasons about the most likely consequence(s) of a sequence of events. Reasoning of any complexity and depth involving computational processes, however, is often opaque and challenging to comprehend. Arguably, for decision makers who may need to evaluate or question the results of autonomous reasoning, it would be useful to be able to inspect the steps involved in an interactive graphical format. When a chain of evidence and constraint-based decision points can be visualized, it becomes easier to explore both how and why a scenario of interest will likely unfold in a particular way. In initial work on a scheme for visualizing cognitively-based decision processes, we focus on generating graphical representations of models run in the Polyscheme cognitive architecture. Our visualization algorithm operates on a modi ed version of Polyscheme's output, which is accomplished by augmenting models with a simple set of tags. We provide example visualizations and discuss properties of our technique that pose challenges for our representation goals. We conclude with a summary of feedback solicited from domain experts and practitioners in the eld of cognitive modeling. ; SPIE Visualization and Data Analysis, v8654, Burlingame, CA, 4-6 Feb 2013.
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Keyword:
*COGNITIVE SCIENCE; COGNITIVE MODELS; Cybernetics; GRAPH NETWORK DATA; GRAPHS; INFORMATION SCIENCES; MODELS; SOCIAL SCIENCES; SOFTWARE VISUALIZATION; SYMPOSIA; VISUAL KNOWLEDGE REPRESENTATION; VISUALIZATION
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URL: http://www.dtic.mil/docs/citations/ADA602495
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA602495
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| 2 |
Automated Extraction and Characterisation of Social Network Data from Unstructured Sources -- An Ontology-Based Approach
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In: DTIC (2013)
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| 3 |
Interacting with Multi-Robot Systems Using BML
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In: DTIC (2013)
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| 5 |
Machine Recognition vs Human Recognition of Voices
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In: DTIC (2012)
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| 6 |
Compressed Domain Automatic Level Control Based on ITU-T G.722.2
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In: DTIC (2012)
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| 7 |
Learning for Microblogs with Distant Supervision: Political Forecasting with Twitter
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In: DTIC (2012)
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| 8 |
Incremental Syntactic Language Models for Phrase-Based Translation
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In: DTIC (2011)
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| 9 |
Trends in Human-Computer Interaction to Support Future Intelligence Analysis Capabilities
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In: DTIC (2011)
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| 10 |
Maritime Domain Awareness via Agent Learning and Collaboration
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In: DTIC (2010)
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| 11 |
The Impact of Interpretation Problems on Tutorial Dialogue
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In: DTIC (2010)
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| 12 |
A Multi-Disciplinary University Research Initiative in Hard and Soft Information Fusion: Overview, Research Strategies and Initial Results
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In: DTIC (2010)
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| 13 |
Towards Real-time Program Awareness via Lexical Link Analysis
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In: DTIC (2010)
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| 14 |
TIACRITIS System and Textbook: Learning Intelligence Analysis through Practice
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In: DTIC (2010)
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| 15 |
Recognizing Connotative Meaning in Military Chat Communications
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In: DTIC (2009)
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| 16 |
Related Entity Finding Based on Co-Occurrence
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In: DTIC (2009)
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| 18 |
THUIR at TREC 2009 Web Track: Finding Relevant and Diverse Results for Large Scale Web Search
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In: DTIC (2009)
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| 19 |
The Multi-Session Audio Research Project (MARP) Corpus: Goals, Design and Initial Findings
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In: DTIC (2009)
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| 20 |
A Hybrid Method for Opinion Finding Task (KUNLP at TREC 2008 Blog Track)
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In: DTIC (2008)
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