Titlebook: Unmanaging; Opening up the Organ Theodore Taptiklis Book 2008 Palgrave Macmillan, a division of Macmillan Publishers Limited 2008 managemen

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icles for knowledge representation it now seems clear that these systems are completely inadequate. In this paper we investigate the alternative posed by the non-normal modal logics and discuss techniques for constructing efficient automated theorem provers for these systems.

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y produce the optimal path at the time of planning. Experimental results are presented verifying the effectiveness and versatility of the algorithm in both predictable and imperfectly predictable time varying environments.

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Ralph Stacey,Norbert Elias This large amount of data enabled us to train large machine learning models such as deep neural networks, which can more precisely predict player decision making in cooperative games. The results with our machine learning models using crowdsourced data were similar to those of laboratory experiments.

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Theodore Taptiklismomentum or Nesterov accelerated gradient is also studied. Numerical experiments show that the infinity norm can provide better performance in finite time gradient descent algorithms and give strong robustness under different network structures.

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