Titlebook: Decision Making under Constraints; Martine Ceberio,Vladik Kreinovich Book 2020 Springer Nature Switzerland AG 2020 Computational Intellige

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外來

,Plans Are Worthless but Planning Is Everything: A Theoretical Explanation of Eisenhower’s Observatiy changing circumstances is often not a good idea, the existence of a pre-computed original plan enables us to produce an almost-optimal strategy—a strategy that would have been computationally difficult to produce on a short notice without the pre-existing plan.

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Why Convex Optimization Is Ubiquitous and Why Pessimism Is Widely Spread,about human decision making. This explanation also helps us explain why in decision making under uncertainty, people often make pessimistic decisions, i.e., decisions based more on the worst-case scenarios.

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Using Constraint Propagation for Cooperative UAV Localization from Vision and Ranging, measurements, by using set inversion via interval analysis?(Moore in Interval analysis. Prentice Hall, 1966 [.]). Then, through position boxes exchange, positions are cooperatively refined by constraint propagation in the group. Results are presented with real robot data, and show position accuracy improvement thanks to cooperation.

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Shangpu Jiang,Daniel Lowd,Dejing Dou. The major reason why learning in neural networks is slow is that neural networks are currently unable to take prior knowledge into account. As a result, they simply ignore this knowledge and simulate learning “from scratch”. In this paper, we show how neural networks can take prior knowledge into account and thus, hopefully, learn faster.

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