COMMITTEES OF LEARNING AGENTS

2000 ◽  
Vol 08 (02) ◽  
pp. 187-202 ◽  
Author(s):  
LARS ASKER ◽  
MATS DANIELSON ◽  
LOVE EKENBERG
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Decision Analysis ◽  
Automatic System ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Control Room ◽  
Learning Agents ◽  
Learning Techniques ◽  
Current Context

We describe how machine learning and decision theory is combined in an application that supports control room operators of a combined heating and power plant to cope with the overwhelming complexity of situations when severe plant disturbances occur. The application is designed as an assistant, rather than as an automatic system that intervenes directly in the operator/plant loop. The application is required to handle vague and numerically imprecise background information in the construction of classifier committees. A classifier committee (or ensemble) is a classifier created by combining the predictions of multiple sub-classifiers. The presented method combines classifiers into a committee by using computational methods for decision analysis that are designed to work when the information at hand is imprecise. The application evaluates and make priorities between classified alarms according to credibilities that depend on the current context. Machine learning techniques are used to construct classifiers that recognize various malfunctions in a process, determine whether a situation is normal or not, and make priorities among alarms.

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning for Agents and Multi-Agent Systems

2011 ◽  
pp. 1-26
Author(s):  
Daniel Kudenko ◽  
Dimitar Kazakov ◽  
Eduardo Alonso
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Learning Agents ◽  
Agent Systems ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Multi Agent ◽  
The Relationship

In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multi-agent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems. Specifically, we focus on the challenges involved in adapting (originally disembodied) machine learning techniques to situated agents, the relationship between learning and communication, learning to collaborate and compete, learning of roles, evolution and natural selection, and distributed learning. In the second part of the chapter, we focus on some practicalities and present two case studies.

Machine Learning for Agents and Multi-Agent Systems

2011 ◽  
pp. 403-420
Author(s):  
Daniel Kudenko ◽  
Dimitar Kazakov ◽  
Eduardo Alonso
Keyword(s):  
Machine Learning ◽  
Autonomous Agents ◽  
Machine Learning Techniques ◽  
Multi Agent Systems ◽  
Learning Agents ◽  
Agent Systems ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Multi Agent ◽  
The Relationship

In order to be truly autonomous, agents need the ability to learn from and adapt to the environment and other agents. This chapter introduces key concepts of machine learning and how they apply to agent and multi-agent systems. Rather than present a comprehensive survey, we discuss a number of issues that we believe are important in the design of learning agents and multi-agent systems. Specifically, we focus on the challenges involved in adapting (originally disembodied) machine learning techniques to situated agents, the relationship between learning and communication, learning to collaborate and compete, learning of roles, evolution and natural selection, and distributed learning. In the second part of the chapter, we focus on some practicalities and present two case studies.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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