Probabilistic AI planning methods that minimize expected execution cost have a neutral attitude towards risk. We demonstrate how one can transform planning problems for risk-sensi...
In this paper we consider decision making under hierarchical imprecise uncertainty models and derive general algorithms to determine optimal actions. Numerical examples illustrate...
We consider the problem of cooperative multiagent planning under uncertainty, formalized as a decentralized partially observable Markov decision process (Dec-POMDP). Unfortunately...
Matthijs T. J. Spaan, Frans A. Oliehoek, Nikos A. ...
We analyze a model of irreversible investment with two sources of uncertainty. A riskneutral decision maker has the choice between two mutually exclusive projects under input pric...
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 ...