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


Multi-Objective Decision Making Multi-Objective Decision Making
Diederik Roijers, Shimon Whiteson
Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can...
Publication Date: April, 2017

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Lifelong Machine Learning Lifelong Machine Learning
Zhiyuan Chen, Bing Liu
Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and...
Publication Date: November, 2016

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Statistical Relational Artificial Intelligence Statistical Relational Artificial Intelligence
Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole
An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cop...
Publication Date: March, 2016

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Essentials of Game Theory (E-Book Only) Essentials of Game Theory (E-Book Only)
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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Representing and Reasoning with Qualitative Preferences Representing and Reasoning with Qualitative Preferences
Ganesh Ram Santhanam, Samik Basu, Vasant Honavar
This book provides a tutorial introduction to modern techniques for representing and reasoning about qualitative preferences with respect to a set of alternatives. The syntax and semantics of several languages for representing preference languages, i...
Publication Date: January, 2016

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Metric Learning Metric Learning
Aurelien Bellet, Amaury Habrard, Marc Sebban,
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many mach...
Publication Date: 01/01/2015

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Graph-Based Semi-Supervised Learning Graph-Based Semi-Supervised Learning
Amarnag Subramanya, Partha Pratim Talukdar,
While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled...
Publication Date: 07/01/2014

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Robot Learning from Human Teachers Robot Learning from Human Teachers
Sonia Chernova , Andrea L. Thomaz ,
Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for ...
Publication Date: 04/01/2014

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General Game Playing General Game Playing
Michael Genesereth, Michael Thielscher,
General game players are computer systems able to play strategy games based solely on formal game descriptions supplied at "runtime" (n other words, they don't know the rules until the game starts). Unlike specialized game players, such as Deep Blue,...
Publication Date: 03/01/2014

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Judgment Aggregation Judgment Aggregation
Davide Grossi, Gabriella Pigozzi,
Judgment aggregation is a mathematical theory of collective decision-making. It concerns the methods whereby individual opinions about logically interconnected issues of interest can, or cannot, be aggregated into one collective stance. Aggregation p...
Publication Date: 03/01/2014

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An Introduction to Constraint-Based Temporal Reasoning An Introduction to Constraint-Based Temporal Reasoning
Roman Bart, Robert A. Morris, K. Brent Venable,
Solving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field. This book provides a concise introduction to the core computational...
Publication Date: 02/01/2014

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Reasoning with Probabilistic and Deterministic Graphical Models Reasoning with Probabilistic and Deterministic Graphical Models
Rina Dechter,
Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. Th...
Publication Date: 12/01/2013

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Introduction to Intelligent Systems in Traffic and Transportation Introduction to Intelligent Systems in Traffic and Transportation
Ana L.C. Bazzan, Franziska Klugl,
Urban mobility is not only one of the pillars of modern economic systems, but also a key issue in the quest for equality of opportunity, once it can improve access to other services. Currently, however, there are a number of negative issues related t...
Publication Date: 12/01/2013

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A Concise Introduction to Models and Methods for Automated Planning A Concise Introduction to Models and Methods for Automated Planning
Hector Geffner, Blai Bonet,
Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertai...
Publication Date: 06/01/2013

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Essential Principles for Autonomous Robotics Essential Principles for Autonomous Robotics
Henry Hexmoor,
From driving, flying, and swimming, to digging for unknown objects in space exploration, autonomous robots take on varied shapes and sizes. In part, autonomous robots are designed to perform tasks that are too dirty, dull, or dangerous for humans. Wi...
Publication Date: 06/01/2013

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Case-Based Reasoning Case-Based Reasoning
Beatriz Lopez,
Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involv...
Publication Date: 11/01/2013

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Trading Agents Trading Agents
Michael Wellman,
Automated trading in electronic markets is one of the most common and consequential applications of autonomous software agents. Design of effective trading strategies requires thorough understanding of how market mechanisms operate, and appreciation ...
Publication Date: 01/01/2011

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Representations and Techniques for 3D Object Recognition and Scene Interpretation Representations and Techniques for 3D Object Recognition and Scene Interpretation
Derek Hoiem, Silvio Savarese,
One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, wi...
Publication Date: 01/01/2011

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Planning with Markov Decision Processes Planning with Markov Decision Processes
Mausam, Andrey Kolobov,
Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for l...
Publication Date: 01/01/2012

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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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