Planning under uncertainty is a central problem in the study of automated sequential
decision making, and has been addressed by researchers in many different fields, including …

The past five years have seen dramatic advances in planning algorithms, with an emphasis
on propositional methods such as GRAPHPLAN and compilers that convert planning …

In this paper, we bring techniques from operations research to bear on the problem of
choosing optimal actions in partially observable stochastic domains. We begin by …

Handbook of Knowledge Representation describes the essential foundations of Knowledge
Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up …

Recent developments have clarified the process of generating partially ordered, partially
specified sequences of actions whose execution will achieve an agent's goal. This article …

Planning in nondeterministic domains yields both conceptual and practical difficulties. From
the conceptual point of view, different notions of planning problems can be devised: for …

Automated sequential decision making is crucial in many contexts. In the face of uncertainty,
this task becomes even more important, though at the same time, computing optimal …

The widespread use of GUIs for interacting with software is leading to the construction of
more and more complex GUIs. With the growing complexity come challenges in testing the …

We define the probabilistic planning problem in terms of a probability distribution over initial
world states, a boolean combination of propositions representing the goal, a probability …

Planning under uncertainty is a difficult task. If sensory information is available, it is possible
to do contingency planning–that is, develop plans where certain branches are executed …