Temporal reasoning

My doctoral research was in the area of temporal reasoning. This, of course, resulted in a dissertation, as well as a number of journal and conference publications.

The primary AI research area of my interest is constraint-based temporal reasoning and, in particular, propagation through a network of temporal constraints under uncertainty. Constraint propagation mechanism is the core element in many temporal reasoning applications including industrial planning and scheduling. I studied how real-world temporal constraints can be represented with some degree of uncertainty. Among probability, intervals, and possibility I chose possibility theory as a compromise between the representative power and computational complexity. I proposed a new representation for approximations of possibilistic distributions, which is substantially different from other previously studied representations. I also studied how such possibilistic representation can be applied in temporal constraint networks and how this representation can be used to extend Petri nets.

• Possiblistic time
• Petri nets and time
• Industrial simulation

Dissertation advisor

• Dr. Raja Loganantharaj

Selected publications

• S. Kurkovsky, R. Loganantharaj. Extension of Petri Nets for Representing and Reasoning with Tasks with Imprecise Durations. Applied Intelligence journal, special issue on Temporal Uncertainty, Springer Science, 2005. DOI
• S. Kurkovsky, R. Loganantharaj. Modeling of, and Reasoning with Recurrent Events with Imprecise Durations. In Proceedings of The 13th International Conference on Industrial And Engineering Applications of Artificial Intelligence And Expert Systems (IEA/AIE-2000), New Orleans, LA, June 2000, Lecture Notes in Computer Science, Vol. 1821, pp. 272-283, Springer Verlag. LNCS
• S. Kurkovsky, R, Loganantharaj. Extension of Petri Nets and its Applications to Model Systems with Imprecise Task Duration. In Proceedings of The 9th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2000), San Antonio, TX, May 2000. DOI
• S. Kurkovsky. Possibilistic Temporal Propagation. PhD Thesis. Center for Advanced Computer Studies, University of Louisiana, Lafayette, 1999.
• R. Loganantharaj, S. Kurkovsky. A New Model for Projecting Temporal Distance Using Fuzzy Temporal Constraints. In Proceedings of The 10th International Conference on Industrial Engineering Applications of Artificial Intelligence Expert Systems (IEA/AIE-97), Atlanta, GA, June 1997.