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.

Autorentext

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.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.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, an 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 Achievement Award. In the 2014âEUR"15 academic year he was a Leverhulme Trust visiting professor atthe University of Oxford.

Inhalt
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.