Postprint version. Published in International Conference on Logic Programming (ICLP’99) Proceedings: Las Cruces, NM, August 13, 1999, pages 109-123.
NOTE: At the time of publication, the author Alex Dekhtyar was not yet affiliated with Cal Poly.
There are many applications where the precise time at which an event will occur (or has occurred) is uncertain. Temporal probabilistic logic programs (TPLPs) allow a programmer to express knowledge about such events. In this paper, we develop a model theory, fixpoint theory, and proof theory for TPLPs, and show that the fixpoint theory may be used to enumerate consequences of a TPLP in a sound and complete manner. Likewise the proof theory provides a sound and complete inference system. Last, but not least, we provide complexity results for TPLPs, showing in particular, that reasonable classes of TPLPs have polynomial data complexity.