کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4942317 | 1437191 | 2016 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A dual-process Qualia Modeling Framework (QMF)
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کلمات کلیدی
IITQMFGWTDPTSMEKNNUCIk-Nearest Neighbors - K نزدیک ترین همسایگانGlobal Workspace Theory - تئوری فضای کاری جهانیWorking memory - حافظه فعال یا حافظه کاریProcedural memory - حافظه پروندهConsciousness - خودآگاهی یا ماهیت ذهنDecision tree - درخت تصمیمSubject matter expert - متخصص موضوعDual-process theory - نظریه دو مرحله ایartificial intelligence - هوش مصنوعیQualia - کوبیاMachine learning - یادگیری ماشین
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
We present the software implementation of our Qualia Modeling Framework (QMF), a computational cognitive model based on the dual-process theory, which theorizes that reasoning and decision making rely on integrated experiences from two interactive minds: the autonomous mind, without the agent's conscious awareness, and the reflective mind, of which the agent is consciously aware. In the QMF, artificial qualia are the vocabulary of the conscious mind, required to reason over conceptual memory, and generate cognitive inferences. The autonomous mind employs pattern-matching, for fast reasoning over episodic memories. An ACT-R model with conventional declarative memory represents the autonomous mind. A second ACT-R model, with an unconventional implementation of declarative memory utilizing a hypernetwork theory based model of qualia space, represents the reflective mind. Using real-world, non-trivial, data sets, our cognitive model achieved classification accuracy comparable to, or greater than, analogous machine learning classifiers kNN and DT, while providing improvements in flexibility by allowing the Target Attribute to be identified or changed any time during training and testing. We advance the BICA challenge by providing a generalizable, efficient, algorithm which models the phenomenal structure of consciousness as proposed by a contemporary theory, and provides an effective decision aid in complex environments where data are too broad or diverse for a human to evaluate without computational assistance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biologically Inspired Cognitive Architectures - Volume 17, July 2016, Pages 71-85
Journal: Biologically Inspired Cognitive Architectures - Volume 17, July 2016, Pages 71-85
نویسندگان
Sandra L. Vaughan, Robert F. Mills, Gilbert L. Peterson, Michael R. Grimaila, Steven K. Rogers, Mark E. Oxley, Robert E. Patterson,