Representing and Reasoning with Qualitative Preferences

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.

• 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 .