NDLI: Cognitive Control: Theory and Application

Content Provider	IEEE Xplore Digital Library
Author	Fatemi, M. Haykin, S.
Copyright Year	2013
Abstract	From an engineering point-of-view, cognitive control is inspired by the prefrontal cortex of the human brain; cognitive control may therefore be viewed as the overarching function of a cognitive dynamic system. In this paper, we describe a new way of thinking about cognitive control that embodies two basic components: learning and planning, both of which are based on two notions: 1) two-state model of the environment and the perceptor and 2) perception-action cycle, which is a distinctive characteristic of the cognitive dynamic system. Most importantly, it is shown that the cognitive control learning algorithm is a special form of Bellman's dynamic programming. Distinctive properties of the new algorithm include the following: 1) optimality of performance; 2) algorithmic convergence to optimal policy; and 3) linear law of complexity measured in terms of the number of actions taken by the cognitive controller on the environment. To validate these intrinsic properties of the algorithm, a computational experiment is presented, which involves a cognitive tracking radar that is known to closely mimic the visual brain. The experiment illustrates two different scenarios: 1) the impact of planning on learning curves of the new cognitive controller and 2) comparison of the learning curves of three different controllers, based on dynamic optimization, traditional \(Q\) -learning, and the new algorithm. The latter two algorithms are based on the two-state model, and they both involve the use of planning.
Starting Page	698
Ending Page	710
Page Count	13
File Size	2469462
File Format	PDF
ISSN	21693536
Volume Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cognition Heuristic algorithms Brain modeling Radar tracking Dynamic programming Complexity theory Perception Control systems Bayesian filtering Cognitive dyanamic systems cognitive control dynamic programming two-state model entropic state Shannon's entropy explore/exploit tradeoff learning planning
Content Type	Text
Resource Type	Article
Subject	Computer Science Engineering Materials Science

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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