We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as ...
We propose a frameworkfor robot programming which allows the seamless integration of explicit agent programming with decision-theoretic planning. Specifically, the DTGolog model a...
Craig Boutilier, Raymond Reiter, Mikhail Soutchans...
—Time and uncertainty of the environment are very important aspects in the development of real world applications. Another important issue for the real world agents is, the balan...
Preferences and uncertainty are common in many real-life problems. In this paper, we focus on bipolar preferences and on uncertainty modelled via uncontrollable variables. However,...
Stefano Bistarelli, Maria Silvia Pini, Francesca R...
This work focuses on an emerging extension to traditional agent models, called Hierarchical Mobile Agents model, where an agent can contain other agents recursively. The model ena...