Representing and Reasoning with Qualitative Preferences: Tools and Applications(Synthesis Lectures on Artificial Intelligence and Machine Learning)

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, including CP-nets, TCP-nets, CI-nets, and CP-theories, are reviewed. Some key problems in reasoning about preferences are introduced, including determining whether one alternative is preferred to another, or whether they are equivalent, with respect to a given set of preferences. These tasks can be reduced to model checking in temporal logic. Specifically, an induced preference graph that represents a given set of preferences can be efficiently encoded using a Kripke Structure for Computational Tree Logic (CTL). One can translate preference queries with respect to a set of preferences into an equivalent set of formulae in CTL, such that the CTL formula is satisfied whenever the preference query holds. This allows us to use a model checker toreason about preferences, i.e., answer preference queries, and to obtain a justification as to why a preference query is satisfied (or not) with respect to a set of preferences. This book defines the notions of the equivalence of two sets of preferences, including what it means for one set of preferences to subsume another, and shows how to answer preferential equivalence and subsumption queries using model checking. Furthermore, this book demontrates how to generate alternatives ordered by preference, along with providing ways to deal with inconsistent preference specifications. A description of CRISNER—an open source software implementation of the model checking approach to qualitative preference reasoning in CP-nets, TCP-nets, and CP-theories is included, as well as examples illustrating its use.

Table of Contents:
Acknowledgments.- Qualitative Preferences.- Qualitative Preference Languages.- Model Checking and Computation Tree Logic.- Dominance Testing via Model Checking.- Verifying Preference Equivalence and Subsumption.- Ordering Alternatives With Respect to Preference.- CRISNER: A Practically Efficient Reasoner for Qualitative Preferences.- Postscript.- Bibliography.- Authors' Biographies .

About the Author :
Ganesh Ram Santhanam is an Associate Scientist at the Department of Electrical and Computer Engineering at Iowa State University. He received his Ph.D. in computer science from Iowa State University in 2010. His research interests include knowledge representation and reasoning, computational decision theory, software engineering, and cyber-security. His doctoral dissertation focused on model checking-based approaches to reasoning with qualitative preferences, and preference reasoning for cyber-defense applications. He has published over 20 research articles on these topics in major journals and conferences in artificial intelligence and software engineering.Samik Basu is a professor of computer science at Iowa State University. He received his Ph.D. in computer science from the State University of New York at Stony Brook in 2003. His research focuses on formal specification and verification of systems, and the application of logic-based techniques to address safety, security, andoptimization problems for software and network-based systems. His research has been funded by several grants from the National Science Foundation. He has published over 70 research articles in major journals and conferences.Vasant Honavar is professor of information sciences and technology and of computer science at the Pennsylvania State University where he holds the Edward Frymoyer Endowed Chair, and heads the artificial intelligence Research Laboratory and the Center for Big Data Analytics and Discovery Informatics. He received his Ph.D. specializing in artificial intelligence from the University of Wisconsin at Madison in 1990. Honavar's current research and teaching interests include artificial intelligence, machine learning, bioinformatics, big data analytics, discovery informatics, social informatics, security informatics, and health informatics. Honavar has led research projects funded by National Science Foundation, the National Institutes of Health, the United States Department of Agriculture, and the Department of Defense that have resulted in foundational research contributions (documented in over 250 peer-reviewed publications) in scalable approaches to building predictive models from large, distributed, semantically disparate data (big data); constructing predictive models from sequence, image, text, multi-relational, graph-structured data; eliciting causal information from multiple sources of observational and experimental data; selective sharing of knowledge across disparate knowledge bases; representing and reasoning about preferences; composing complex services from components; and applications in bioinformatics, social network informatics, health informatics, energy informatics, and security informatics.