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 cope with uncertainty.
Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations.
The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
|Publisher:||Morgan and Claypool Publishers|
|Series:||Synthesis Lectures on Artificial Intelligence and Machine Le|
|Product dimensions:||7.50(w) x 9.30(h) x 0.00(d)|
About the Author
Luc De Raedt is a full professor of computer science at the KU Leuven (Belgium), where he is director of the Lab for Declarative Languages and Artificial Intelligence, and where he also obtained his Ph.D. He is also a former professor of computer science of the Albert-Ludwigs University Freiburg (Germany) and chair of its lab for Natural Language Processsing and Machine Learning.
Luc De Raedt's research interests are in artificial intelligence, machine learning, and data mining, as well as their applications. He is currently working on probabilistic logic learning (sometimes called statistical relational learning), which combines probabilistic reasoning methods with logical representations and machine learning, the integration of constraint programming with data mining and machine learning principles, the development of programming languages for machine learning, and analyzing graph and network data. He is also interested in applications of these methods to chemo- and bio-informatics, to natural language processing, vision, robotics, and action and activity learning. He was program (co)-chair of the 7th ECML Machine Learning (1994, Catania, Sicily), the 5th ILP (1995, Leuven, Belgium), the first ECMLPKDD (2001, Freiburg, Germany), the 22nd ICML Learning (2005, Bonn, Germany) and the 20th ECAI (2012, Montpellier, France). He is an area/action editor of TPLP, JMLR, MLJ, AIJ, and formerly of JAIR. He is also a member of the editorial boards of NGC, AI Communications, Informatica, DMKD, and the Journal of Applied Logic. He was an elected and founding member of the board of the International Machine Learning Society from 2004–2011. In 2005, he was elected as an ECCAI fellow and four of his students have won the ECCAI dissertation award for the best European dissertation in AI.
Kristian Kersting is an associate professor in the Computer Science Department at the Technical University of Dortmund, Germany. He received his Ph.D. from the University of Freiburg, Germany in 2006 and moved to the Fraunhofer IAIS and the University of Bonn using a Fraunhofer ATTRACT Fellowship in 2008 after a PostDoc at MIT, Cambridge, MA, U.S. Before moving to the TU Dortmund University in 2013, he was appointed Assistant Professor for Spatio-Temporal Patterns in Agriculture at the University of Bonn in 2012. Additionally, he was adjunct assistant professor at the Medical School of the Wake Forest University, Winston-Salem, NC, U.S., in 2012. His main research interests are data mining, machine learning, and statistical relational artificial intelligence. He has published over 120 peer-reviewed papers and received the ECCAI Dissertation Award 2006, the ECML Best Student Paper Award in 2006, the ACM SIGSPATIAL GIS Best Poster Award in 2011, and the AAAI-2013 Outstanding PC Member Award. He has given several tutorials at top conferences and co-chaired BUDA, CMPL, Co- LISD, MLG, SRL, and StarAI, as well as the AAAI Student Abstract track and the Starting AI Research Symposium (STAIRS). Together with Stuart Russell, Leslie Kaelbling, Alon Halevy, Sriraam Natarajan, and Lilyana Mihalkova he cofounded the international workshop series on Statistical Relational AI (StarAI). He served as area chair/senior PC for several top conferences and co-chaired ECML PKDD 2013, the premier European venue for Machine Learning and Data Mining. Currently, he is an action editor of AIJ, DAMI, JAIR, and MLJ as well as on the editorial board of NGC.
Sriraam Natarajan is an assistant professor at Indiana University. He was previously an assistant professor at Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison, and graduated with his Ph.D. from Oregon State University. His research interests lie in the field of artificial intelligence, with emphasis on machine learning, statistical relational learning and AI, reinforcement learning, graphical models, and biomedical applications. He has received the Young Investigator award from U.S. Army Research Office. He is the organizer of the key workshops in the field of Statistical Relational Learning and has co-organized the AAAI 2010, the UAI 2012, AAAI 2013, and AAAI 2014 workshops on Statistical Relational AI (StarAI), ICML 2012 Workshop on Statistical Relational Learning, and the ECML PKDD 2011 and 2012 workshops on Collective Learning and Inference on Structured Data (Co-LISD). He is also the co-chair of the AAAI student abstract and posters at AAAI 2014 and AAAI 2015.
David Poole is a professor of computer science at the University of British Columbia. He has a Ph.D. from the Australian National University. He is known for his work on assumption based reasoning, diagnosis, relational probabilistic models, combining logic and probability, algorithms for probabilistic inference, representations for automated decision making, probabilistic reasoning with ontologies, and semantic science. He is a co-author of a new AI textbook, Artificial Intelligence: Foundations of Computational Agents (Cambridge University Press, 2010), co-author of an older AI textbook, Computational Intelligence: A Logical Approach (Oxford University Press, 1998), co-chair of AAAI-10 (twenty-Fourth AAAI Conference on Artificial Intelligence), and co-editor of the Proceedings of the Tenth Conference in Uncertainty in Artificial Intelligence (Morgan Kaufmann, 1994). He is a former associate editor of the Journal of AI Research, and the AI Journal, and the editorial board of AI Magazine. He is the chair of the Association for Uncertainty in Artificial Intelligence and is a Fellow of the Association for the Advancement Artificial Intelligence (AAAI). He is the winner of the Canadian AI Association (CAIAC) 2013 Lifetime AchieveAUTHORS’ BIOGRAPHIES 171 ment Award. In the 2014–15 academic year he was a Leverhulme Trust visiting professor at the University of Oxford.
Table of Contents
Table of Contents: Preface / Motivation / Statistical and Relational AI Representations / Relational Probabilistic Representations / Representational Issues / Inference in Propositional Models / Inference in Relational Probabilistic Models / Learning Probabilistic and Logical Models / Learning Probabilistic Relational Models / Beyond Basic Probabilistic Inference and Learning / Conclusions / Bibliography / Authors' Biographies / Index