An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

Zhang, Xiaoqin Shelley; Shrestha, Bhavesh; Yoon, Sungwook; Kambhampati, Subbarao; DiBona, Phillip; Guo, Jinhong K.; McFarlane, Daniel C.; Hofmann, Martin; Whitebread, Kenneth R.; Appling, Darren Scott; Whitaker, Elizabeth; Trewhitt, Ethan; Ding, Li; Michaelis, James; McGuinness, Deborah L.; Hendler, James A.; Doppa, Janardhan Rao; Parker, Charles; Dietterich, Thomas G.; Tadepalli, Prasad; Wong, Weng‐Keen; Green, Derek; Rebguns, Anton; Spears, Diana F.; Kuter, Ugur; Levine, Geoff; DeJong, Gerald; MacTavish, Reid; Ontañón, Santiago; Radhakrishnan, Jainarayan; Ram, Ashwin; Mostafa, Hala; Zafar, Huzaifa; Zhang, Chongjie; Corkill, Daniel D.; Lesser, Victor; Song, Zhexuan

doi:10.1145/2337542.2337560

Cited by 11 publications

(3 citation statements)

References 34 publications

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“…GILA (Generalized Integrated Learning Architecture) [51] is based on the combined use of several heterogeneous and independent problem-solving and learning methods, using an ensemble architecture in which a central metareasoning executive (MRE) controls the processing. It is aimed at addressing problems in difficult real-world domains, where tasks are complex with multiple interacting subproblems, and where near-optimal solutions are called for.…”

Section: Comparison With Other Systemsmentioning

confidence: 99%

A learning system based on lazy metareasoning

Houeland

Aamodt

2017

Prog Artif Intell

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Metareasoning has been widely studied in the literature, with a wide variety of algorithms and partially overlapping methodological approaches. However, these methods are typically either not targeted toward practical machine learning systems or alternatively are focused on achieving the best possible performance for a particular domain, with extensive human tuning and research, and vast computing resources. In this paper, our goal is to create systems that perform sustained autonomous learning, with automatically determined domain-specific optimizations for any given domain, and without requiring human assistance. We present Alma, a metareasoning architecture that creates and selects reasoning methods based on empirically observed performance. This is achieved by using lazy learning at the metalevel, and automatically training and combining reasoning methods at run-time. In experiments across diverse data sets, we demonstrate the ability of Alma to successfully reason about learner performance in different domains and achieve a better overall result than any of the individual reasoning methods, even with limited computing time available.

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Section: Comparison With Other Systemsmentioning

confidence: 99%

A learning system based on lazy metareasoning

Houeland

Aamodt

2017

Prog Artif Intell

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“…Different from classical ensemble learning, deep ensemble learning tries to tackle the problem of multimodal analysis and extends the bound of generalization ability of the ensemble learner. A key advantage of deep ensemble learning is that deep models can be used as base learners, which extends the representation capability to a great extent [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD‐10 Labels

Zhang

Huang

Zhong

et al. 2015

The Scientific World Journal

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Objective. This study aims to establish a model to analyze clinical experience of TCM veteran doctors. We propose an ensemble learning based framework to analyze clinical records with ICD-10 labels information for effective diagnosis and acupoints recommendation. Methods. We propose an ensemble learning framework for the analysis task. A set of base learners composed of decision tree (DT) and support vector machine (SVM) are trained by bootstrapping the training dataset. The base learners are sorted by accuracy and diversity through nondominated sort (NDS) algorithm and combined through a deep ensemble learning strategy. Results. We evaluate the proposed method with comparison to two currently successful methods on a clinical diagnosis dataset with manually labeled ICD-10 information. ICD-10 label annotation and acupoints recommendation are evaluated for three methods. The proposed method achieves an accuracy rate of 88.2% ± 2.8% measured by zero-one loss for the first evaluation session and 79.6% ± 3.6% measured by Hamming loss, which are superior to the other two methods. Conclusion. The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records. The computational cost of training a set of base learners is relatively low.

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“…In Chung and Sang [2000], memory based reasoning (MBR) and neural network (NN) learning are combined for yield improvement and an integrated framework is proposed for a yield management system based on hybrid machine learning techniques. In Zhang et al [2012], a set of heterogeneous independent learning components were coordinated by a central metareasoning executive. It has been demonstrated that machine learning techniques assist in managing complexity, diversity, and uncertainties in manufacturing process management [Monostori 2003].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Optimization of Order Admission Policies in a Digital Print Factory

Duan

Zeng

Chakrabarty

et al. 2015

ACM Trans. Des. Autom. Electron. Syst.

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On-demand digital print service is an example of a real-time embedded enterprise system. It offers mass customization and exemplifies personalized manufacturing services. Once a print order is submitted to the print factory by a client, the print service provider (PSP) needs to make a real-time decision on whether to accept or refuse this order. Based on the print factory's current capacity and the order's properties and requirements, an order is refused if its acceptance is not profitable for the PSP. The order is accepted with the most appropriate due date in order to maximize the profit that can result from this order. We have developed an automated learning-based order admission framework that can be embedded into an enterprise environment to provide real-time admission decisions for new orders. The framework consists of three classifiers: Support Vector Machine (SVM), Decision Tree (DT), and Bayesian Probabilistic Model (BPM). The classifiers are trained by history orders and used to predict completion status for new orders. A decision integration technique is implemented to combine the results of the classifiers and predict due dates. Experimental results derived using real factory data from a leading print service provider and Weka open-source software show that the order completion status prediction accuracy is significantly improved by the decision integration strategy. The proposed multiclassifier model also outperforms a standalone regression model.

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An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

Cited by 11 publications

References 34 publications

A learning system based on lazy metareasoning

A learning system based on lazy metareasoning

An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD‐10 Labels

Data-Driven Optimization of Order Admission Policies in a Digital Print Factory

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