Reinforcement learning driven adaptive active frequency drift for fast and reliable islanding detection. [PDF]
Abo-Khalil AG +5 more
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Center-surround inhibition in expectation and its underlying computational and artificial neural network models. [PDF]
Huang L +6 more
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Development of thermal memory cells on silicon using the floating zero algorithm. [PDF]
Kulchin YN +3 more
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Intelligent Patient Management in Viral Diseases: An Integrated Regression Model and Multi-Criteria Decision-Making Approach to Convalescent Plasma Transfusion. [PDF]
Mohammed TJ +5 more
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State-trace analysis meets personality measurement: Why the Big Five tests are not based on five latent dimensions and how to fix them. [PDF]
Titz J.
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Towards a non monotonic description logics model
Martín O. Moguillansky +1 more
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Abstract ‘Non-monotonic’ logical systems are logics in which the introduction of new axioms can invalidate old theorems. Such logics are very important in modeling the beliefs of active processes which, acting in the presence of incomplete information, must make and subsequently revise assumptions in light of new observations.
McDermott, Drew, Doyle, Jon
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AbstractThis paper is a review of certain non-monotonic logics, which I call default non-monotonic logics. These are logics which exploit failure to prove. How each logic uses this basic idea is explained, and examples given. The emphasis is on leading ideas explained through examples: technical detail is avoided.
P. Mott
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Psychological laws and non-monotonic logic
Erkenntnis, 1996In this essay I enter into a recently published debate between Stephen Schiffer and Jerry Fodor concerning whether adequate sense can be made of the ceteris paribus conditions in special science laws, much of their focus being on the case of putative psychological laws.
A. Silverberg
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Intuitionistic basis for non-monotonic logic
CADE, 2005McDermott and Doyle [4] suggested a system, denoted by ⊢, of non-monotonic logic. This notion was intended to formalise non-monotonic reasoning as involved in real situations and in artificial intelligence. McDermott and Doyle also list in their paper several difficulties and problems in their approach.
D. Gabbay
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