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

• Available here