knowledge gradient
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2021 ◽  
pp. 146144562110168
Author(s):  
Paulien Harms ◽  
Tom Koole ◽  
Ninke Stukker ◽  
Jaap Tulleken

This paper examines how expertise is treated as a separable domain of epistemics by looking at simulated intensive care shift-handovers between resident physicians. In these handovers, medical information about a patient is transferred from an outgoing physician (OP) to an incoming physician (IP). These handovers contain different interactional activities, such as discussing the patient identifiers, giving a clinical impression, and discussing tasks and focus points. We found that with respect to (factual) knowledge about the patient, the OPs display an orientation to a knowledge imbalance, but with respect to (clinical) procedures, reasoning, and activities, they display an orientation to a knowledge balance. We use ‘expertise’ to refer to this latter type of knowledge. ‘Expertise’ differs from, and adds to, how knowledge is often treated in epistemics in that it is concerned with professional competence or ‘knowing how’. In terms of epistemics, the participants in the handovers orient to a steep epistemic or knowledge gradient when it concerns the patient, while simultaneously displaying an orientation to a horizontal expertise gradient.


Author(s):  
Siyu Tao ◽  
Anton van Beek ◽  
Daniel W. Apley ◽  
Wei Chen

Abstract We address the problem of simulation-based design using multiple interconnected expensive simulation models, each modeling a different subsystem. Our goal is to find the globally optimal design with minimal model evaluation costs. To our knowledge, the best existing approach is to treat the whole system as a single expensive model and apply an existing Bayesian optimization (BO) algorithm. This approach is likely inefficient due to the need to evaluate all the component models in each iteration. We propose a multi-model BO approach that dynamically and selectively evaluates one component model per iteration based on linked emulators for uncertainty quantification and the system knowledge gradient (KG) as acquisition function. Building on this, we resolve problems with constraints and feedback couplings that often occur in real complex engineering design by penalizing the objective emulator and reformulating the original problem into a decoupled one. The superior efficiency of our approach is demonstrated through solving an analytical problem and a multidisciplinary design problem of electronic packaging optimization.


Author(s):  
Xiaozhou Wang ◽  
Xi Chen ◽  
Qihang Lin ◽  
Weidong Liu

The performance of clustering depends on an appropriately defined similarity between two items. When the similarity is measured based on human perception, human workers are often employed to estimate a similarity score between items in order to support clustering, leading to a procedure called crowdsourced clustering. Assuming a monetary reward is paid to a worker for each similarity score and assuming the similarities between pairs and workers' reliability have a large diversity, when the budget is limited, it is critical to wisely assign pairs of items to different workers to optimize the clustering result. We model this budget allocation problem as a Markov decision process where item pairs are dynamically assigned to workers based on the historical similarity scores they provided. We propose an optimistic knowledge gradient policy where the assignment of items in each stage is based on the minimum-weight K-cut defined on a similarity graph. We provide simulation studies and real data analysis to demonstrate the performance of the proposed method.


2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Seyede Fatemeh Ghoreishi ◽  
Samuel Friedman ◽  
Douglas L. Allaire

Available computational models for many engineering design applications are both expensive and and of a black-box nature. This renders traditional optimization techniques difficult to apply, including gradient-based optimization and expensive heuristic approaches. For such situations, Bayesian global optimization approaches, that both explore and exploit a true function while building a metamodel of it, are applied. These methods often rely on a set of alternative candidate designs over which a querying policy is designed to search. For even modestly high-dimensional problems, such an alternative set approach can be computationally intractable, due to the reliance on excessive exploration of the design space. To overcome this, we have developed a framework for the optimization of expensive black-box models, which is based on active subspace exploitation and a two-step knowledge gradient policy. We demonstrate our approach on three benchmark problems and a practical aerostructural wing design problem, where our method performs well against traditional direct application of Bayesian global optimization techniques.


2018 ◽  
Vol 30 (4) ◽  
pp. 750-767 ◽  
Author(s):  
Yan Li ◽  
Kristofer G. Reyes ◽  
Jorge Vazquez-Anderson ◽  
Yingfei Wang ◽  
Lydia M. Contreras ◽  
...  

2018 ◽  
Vol 56 (2) ◽  
pp. 1105-1129
Author(s):  
Yingfei Wang ◽  
Warren B. Powell

2016 ◽  
Vol 31 (2) ◽  
pp. 239-263 ◽  
Author(s):  
James Edwards ◽  
Paul Fearnhead ◽  
Kevin Glazebrook

The knowledge gradient (KG) policy was originally proposed for online ranking and selection problems but has recently been adapted for use in online decision-making in general and multi-armed bandit problems (MABs) in particular. We study its use in a class of exponential family MABs and identify weaknesses, including a propensity to take actions which are dominated with respect to both exploitation and exploration. We propose variants of KG which avoid such errors. These new policies include an index heuristic, which deploys a KG approach to develop an approximation to the Gittins index. A numerical study shows this policy to perform well over a range of MABs including those for which index policies are not optimal. While KG does not take dominated actions when bandits are Gaussian, it fails to be index consistent and appears not to enjoy a performance advantage over competitor policies when arms are correlated to compensate for its greater computational demands.


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