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163
Machine learning for fast quadrupedal locomotion
- in The Nineteenth National Conference on Artificial Intelligence
, 2004
"... For a robot, the ability to get from one place to another is one of the most basic skills. However, locomotion on legged robots is a challenging multidimensional control problem. This paper presents a machine learning approach to legged locomotion, with all training done on the physical robots. The ..."
Abstract
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Cited by 123 (34 self)
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. The main contributions are a specification of our fully automated learning environment and a detailed empirical comparison of four different machine learning algorithms for learning quadrupedal locomotion. The resulting learned walk is considerably faster than all previously reported hand-coded walks
Wireless Sensor Networking in Challenging Environments
, 2014
"... This Dissertation is brought to you for free and open access by Washington University Open Scholarship. It has been accepted for inclusion in All Theses and Dissertations (ETDs) by an authorized administrator of Washington University Open Scholarship. For more information, please contact ..."
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This Dissertation is brought to you for free and open access by Washington University Open Scholarship. It has been accepted for inclusion in All Theses and Dissertations (ETDs) by an authorized administrator of Washington University Open Scholarship. For more information, please contact
G.: Beating the defense: Using plan recognition to inform learning agents
- In: Proceedings of Florida Artifical Intelligence Research Society, AAAI
, 2009
"... In this paper, we investigate the hypothesis that plan recognition can significantly improve the performance of a casebased reinforcement learner in an adversarial action selection task. Our environment is a simplification of an American football game. The performance task is to control the behavior ..."
Abstract
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Cited by 15 (9 self)
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, and that it significantly improved performance. More generally, our studies show that plan recognition reduced the dimensionality of the state space, which allowed learning to be conducted more effectively. We describe the algorithms, explain the reasons for performance improvement, and also describe a further empirical
A Comparison of Exploration/Exploitation Techniques for a Q-Learning Agent in the Wumpus World
"... The Q-Learning algorithm, suggested by Watkins [1], has become one of the most popular reinforcement learning algorithms due to its relatively simple implementation and the complexity reduction gained by the use of a model-free method. However, Q-Learning does not specify how to trade off exploratio ..."
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The Q-Learning algorithm, suggested by Watkins [1], has become one of the most popular reinforcement learning algorithms due to its relatively simple implementation and the complexity reduction gained by the use of a model-free method. However, Q-Learning does not specify how to trade off
Computational Neural Network for Global Stock Indexes Prediction
"... Abstract- In this paper, computational data mining methodology was used to predict four major stock market indexes. Two learning algorithms including Linear Regression and Neural Network Standard Back Propagation (SBP) were tested and compared. The models were trained from two years of historical da ..."
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Abstract- In this paper, computational data mining methodology was used to predict four major stock market indexes. Two learning algorithms including Linear Regression and Neural Network Standard Back Propagation (SBP) were tested and compared. The models were trained from two years of historical
Bayesian Model Averaging in Reinforcement Learning by
"... Reinforcement learning is a fundamental problem in Artificial Intelligence where an agent must learn to act in an unknown environment in a way that maximizes a cumulative reward signal. As the number of states in an environment grows larger, the need to generalize learning experience over similar st ..."
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learning work focuses on learning with a single model, whereas different complexity models may be appropriate at different learning stages, e.g., simpler models in the early learning stages and more complex models at later learning stages. In this work, we aim to build an RL algorithm for trading off
LIBRARIES Learning Theory Applications to Product Design Modeling By
, 2000
"... Integrated product development increasingly relies upon simulations to predict design characteristics. However, several problems arise in integrated modeling. First, the computational requirements of many sub-system models can prohibit system level optimization. Further, when several models are chai ..."
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simulations. Also, in many cases sub-system behavior is described through empirical data only. In such cases, surrogate modeling techniques may be applicable as well. This thesis explores the use of Artificial Neural Network (ANN) algorithms in integrated design simulation. In summary, the thesis focuses
Enhanced Indoor Locationing in a Congested Wi-Fi Environment
, 2009
"... Many context-aware mobile applications require a reasonably accurate and stable estimate of a user’s location. While the Global Positioning System (GPS) works quite well world-wide outside of buildings and urban canyons, locating an indoor user in a real-world environment is much more problematic. S ..."
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high room-level accuracy of the indoor location prediction of a mobile user. The Redpin algorithm, in particular, matches the Wi-Fi signal received with the signals in the training data and uses the position of the closest training data as the user's current location. However, in a congested Wi
Teacher Recruitment and Retention: A Review of the Recent Empirical Literature
"... This article critically reviews the recent empirical literature on teacher recruitment and retention published in the United States. It examines the characteristics of individuals who enter and remain in the teaching profession, the characteristics of schools and districts that successfully recruit ..."
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recruit and retain teachers, and the types of policies that show evidence of efficacy in recruiting and retaining teachers. The goal of the article is to provide researchers and policymakers with a review that is comprehensive, evaluative, and up to date. The review of the empirical studies selected
A framework for evaluating approximation methods for Gaussian process regression
- Journal of Machine Learning Research
"... Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine learning. However, even a straightforward implementation of Gaussian process regression (GPR) requires O(n2) space and O(n3) time for a dataset of n examples. Several approximation methods have been pr ..."
Abstract
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Cited by 17 (0 self)
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of Data and FITC). We empirically investigate four different approximation algorithms on four different prediction problems, and make our code available to encourage future comparisons.
Results 1 - 10
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163
