Miroslaw (Mirek) Truszczynski, Ph.D.


Research Areas: Artificial intelligence, constraint satisfaction, declarative programming, knowledge representation and reasoning

University of Kentucky, College of Engineering
Computer Science - CS
309 DMB
Lexington, KY 40506-0633
Phone: 859-257-6738
Fax: 859-257-1505
Email: mirek@cs.uky.edu


M.S. Warsaw University of Technology, 1978

Ph.D. Warsaw University of Technology, 1980


  1. Preferences in artificial intelligence. Preferences are among the most fundamental attributes of human reasoning and decision making. They appear whenever a choice between alternatives is to be made. Buying a car we consider factors such as safety, economy, convenience, appearance. Ordering a dinner we take into account what we like and how hungry we are. I am interested in formal languages with which to model human preferences and tools to reason about them. I stress qualitative methods over the more broadly used quantitative ones. Quantitative methods express preferences in terms of utility functions, but building good utility functions is difficult, error prone and time consuming. Qualitative approaches aim to circumvent this problem by specifying preference orders in terms of simple and intuitive qualitative statements directly about properties of alternatives under consideration. The project is funded by an NSF grant.
  2. Answer-set programming. Answer-set programming is a paradigm to use logics and associated model generating tools for solving search problems. I am  interested in foundations of answer-set programming as a knowledge representation system and in the building effective answer-set programming software. Jointly with a postdoc, Yulia Lierler, who is funded by the CRA/NSF CIFellows program, I am studying the use of transition systems as a unifying formal tool for developing and studying model-generating algorithms, applications of transition systems to multicontext systems, and some database applications of answer-set programming integrated with constraint solving. I also study extensions of an answer-set programming system developed with Deborah East, a PhD student of mine, about 10 years ago, and of the logic FO(ID) developed by Marc Denecker and his research group at the Katholike Universiteit Leuven in Belgium. Jointly with an undergraduate student, David Brown, we are building a new grounder, a tool responsible for the preprocessing of the input program, aiming to extend the set of modeling features in the input language and to improve efficiency.
  3. Applications of answer-set programming in software engineering. Each software project starts with requirements. However, requirements are often inconsistent, which is a common source of project delays and over-budget costs. In this project we are concerned with temporal requirements. Detecting inconsistencies of temporal requirements is essential and should take place before the design phase so that the cost of revisions can be minimized. Automating or partially automating the process is crucial as the task, when performed manually, is time consuming and error-prone. In a joint project with Dr. Jane Hayes, we are developing a high-level language to express temporal requirements arising in software and system specifications. We are studying ways to compile specifications given in that language to answer-set programs in order to determine their consistency. This project is sponsored by a contract from the USASMDC/ARTSTRAT, managed by ONR.



  1. G. Brewka, T. Eiter, M. Truszczynski, Answer-set Programming at a glance,Communications of the ACM 54(12): 92-103, (2011).
  2.  Y. Lierler and M. Truszczynski, Transition systems for model generators -A unifying approach. Theory and Practice of Logic Programming, 11(4-5):629-646 (2011).
  3. M. Truszczynski, Trichotomy and Dichotomy Results on the Complexity of Reasoning with Disjunctive Logic Programs, Theory and Practice of Logic Programming, 11(6): 881-994 (2011).
  4.   M. Truszczynski, Reducts of propositional theories, satisfiability relations, and generalizations of semantics of logic programs. Artificial Intelligence 174(16-17): 1285-1306 (2010).
  5.  G. Brewka M. Truszczynski, S. Woltran,  Representing Preferences Among Sets, Proceedings of Association for the Advancement of Artificial Intelligence Conference, AAAI 2010, AAAI Press.



  1. Image-Net: Discriminatory Imaging and Network Advancement for Missiles, Aviation and Space, US Army Space and Missile Defense Command/US Army Forces Strategic Command, co-PI (B. Seales, PI, eight other co-PIs), $2,092,905, 9/26/2011 – 9/30/2012.
  2. Toward Unambiguous and Consistent Textual Requirements: An Application of Natural Language Processing Techniques, co-PI (Allen Nicora, JPL, PI, Jane Hayes, Victor Marek, co-PIs), April 2010 – October 2010, $106,000.
  3. RI: Small: Qualitative preferences: merging paradigms, extending the language, reasoning about incomplete outcomes, PI NSF, 2009-2012, $384,999
  4. International Cooperation with Vienna University of Technology and two REU supplements to Qualitative preferences: merging paradigms, extending the language, reasoning about incomplete outcomes, PI, NSF, $37,000.