Traditional AI planning representations have lacked explicit representation of
both time and uncertainty. This lack of expressiveness has limited the applicability of traditional AI planning systems to only severly restricted domains. In
order to address more realistic planning problems, researchers have turned to
temporal logics. But the temporally enriched planning representations still lack
the ability to represent uncertainty, as well as lacking any kind of link to a theory of rational behavior. Decision Theory provides a normative model of choice
under uncertainty such that recommended choices can be said to be rational
under a well-defined notion of rationality. But traditional decision-theoretic representations are limited in their ability to structure knowledge other than beliefs
and preferences.