Probabilistic Models in Planning An overview

Planning has been one of the main research areas in AI. For about three decades AI researchers explore alternative paths to build intelligent agents with advanced planning capabilities. However, the classical AI planning techniques suffer from inapplicability to real world domains, due to several assumptions adopted to facilitate research. Attempts to apply planning into real domains must address the problem of uncertainty, which requires a revision of the classical planning framework. Probabilistic models seem to offer a promising alternative, providing models of planning where plans can be represented, generated and evaluated under a standard probabilistic interpretation of uncertainty. This survey paper1 attempts to cover the recent work in this direction and trigger the interest of the reader for further study and exploration.