Artificial Intelligence and Machine Learning

SERIES EDITORS:
Ronald Brachman, Jacobs Technion-Cornell Institute at Cornell Tech
Peter Stone, University of Texas at Austin

Editor Bios


Human Computation Human Computation
Edith Law, Luis von Ahn,
Human computation is a new and evolving research area that centers around harnessing human intelligence to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms. With the growth of the Web, human c...
Publication Date: 01/01/2011

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Computational Aspects of Cooperative Game Theory Computational Aspects of Cooperative Game Theory
Georgios Chalkiadakis, Edith Elkind, Michael Wooldridge,
Cooperative game theory is a branch of (micro-)economics that studies the behavior of self-interested agents in strategic settings where binding agreements among agents are possible. Our aim in this book is to present a survey of work on the computat...
Publication Date: 01/01/2011

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Answer Set Solving in Practice Answer Set Solving in Practice
Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Torsten Schaub,
Answer Set Programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning (KRR). More recently, its attractive combination of a rich yet simple modeling langu...
Publication Date: 01/01/2012

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Active Learning Active Learning
Burr Settles,
The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data insta...
Publication Date: 01/01/2012

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A Short Introduction to Preferences A Short Introduction to Preferences
Francesca Rossi, Kristen Brent Venable, Toby Walsh,
Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity. This book provides a concise intro...
Publication Date: 01/01/2011

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Visual Object Recognition Visual Object Recognition
Kristen Grauman, Bastian Leibe,
The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vis...
Publication Date: 01/01/2011

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Learning with Support Vector Machines Learning with Support Vector Machines
Colin Campbell, Yiming Ying,
Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisat...
Publication Date: 01/01/2011

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Data Integration Data Integration
Michael Genesereth,
Data integration is a critical problem in our increasingly interconnected but inevitably heterogeneous world. There are numerous data sources available in organizational databases and on public information systems like the World Wide Web. Not surpris...
Publication Date: 01/01/2010

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Algorithms for Reinforcement Learning Algorithms for Reinforcement Learning
Csaba Szepesvari,
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is th...
Publication Date: 01/01/2010

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Representation Discovery using Harmonic Analysis Representation Discovery using Harmonic Analysis
Sridhar Mahadevan,
Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes ...
Publication Date: 01/01/2008

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Markov Logic Markov Logic
Pedro Domingos, Daniel Lowd,
Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface...
Publication Date: 01/01/2009

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Introduction to Semi-Supervised Learning Introduction to Semi-Supervised Learning
Xiaojin Zhu, Andrew Goldberg,
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised...
Publication Date: 01/01/2009

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Essentials of Game Theory Essentials of Game Theory
Kevin Leyton-Brown, Yoav Shoham,
Game theory is the mathematical study of interaction among independent, self-interested agents. The audience for game theory has grown dramatically in recent years, and now spans disciplines as diverse as political science, biology, psychology, econo...
Publication Date: 01/01/2008

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Action Programming Languages Action Programming Languages
Michael Thielscher,
Artificial systems that think and behave intelligently are one of the most exciting and challenging goals of Artificial Intelligence. Action Programming is the art and science of devising high-level control strategies for autonomous systems which emp...
Publication Date: 01/01/2008

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Intelligent Autonomous Robotics Intelligent Autonomous Robotics
Peter Stone,
Robotics technology has recently advanced to the point of being widely accessible for relatively low-budget research, as well as for graduate, undergraduate, and even secondary and primary school education. This lecture provides an example of how to ...
Publication Date: 01/01/2007

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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
Nikos Vlassis,
Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering th...
Publication Date: 01/01/2007

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