Artificial Intelligence and Machine Learning

SERIES EDITORS:
Ronald Brachman, Jacobs Technion-Cornell Institute at Cornell Tech
Francesca Rossi, AI Ethics Global Leader, IBM Research AI and Professor of Computer Science, University of Padova
Peter Stone, University of Texas at Austin

Editor Bios

	Reasoning with Probabilistic and Deterministic Graphical Models, 2nd Edition 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… Read More
	Learning and Decision-Making from Rank Data Lirong Xia The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effecti… Publication Date: February, 2019 Read More
	Lifelong Machine Learning Zhiyuan Chen, Bing Liu Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the … Publication Date: August, 2018 Read More
	Adversarial Machine Learning Yevgeniy Vorobeychik, Murat Kantarcioglu The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, financ… Publication Date: August, 2018 Read More
	Strategic Voting Reshef Meir Social choice theory deals with aggregating the preferences of multiple individuals regarding several available alternatives, a situation colloquially known as voting. There are many different voting rules in use and even more in the liter… Publication Date: June, 2018 Read More
	Predicting Human Decision-Making Ariel Rosenfeld, Sarit Kraus Human decision-making often transcends our formal models of “rationality.” Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore… Publication Date: January, 2018 Read More
	Game Theory for Data Science Boi Faltings, Goran Radanovic Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, i… Publication Date: September, 2017 Read More
	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 Read More
	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 Read More
	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 Read More
	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 Read More
	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 Read More
	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 Read More
	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 Read More
	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 Read More
	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 Read More
	An Introduction to Constraint-Based Temporal Reasoning Roman Bartak, 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 Read More
	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 Read More
	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 Read More
	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 Read More