We examine the computational complexity of testing and nding small plans in probabilistic planning domains with both at and propositional representations. The complexity of plan e...
Michael L. Littman, Judy Goldsmith, Martin Mundhen...
A possibilistic approach of planning under uncertainty has been developed recently. It applies to problems in which the initial state is partially known and the actions have graded...
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...
The ability to flexibly compose confidence computation with the operations of relational algebra is an important feature of probabilistic database query languages. Computing confi...
In this paper we present an overview of recent developments in the plan-based control of autonomous robots. We identify computational principles that enable autonomous robots to a...