Despite the recent advances in planning with MDPs, the problem of generating good policies is still hard. This paper describes a way to generate policies in MDPs by (1) determiniz...
We investigate search problems under risk in statespace graphs, with the aim of finding optimal paths for risk-averse agents. We consider problems where uncertainty is due to the...
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...
- This paper presents a model that enables the optimal interpretation of Prognostics and Health Management (PHM) results for electronic systems. In this context, optimal interpreta...
Given an adequate simulation model of the task environment and payoff function that measures the quality of partially successful plans, competition-based heuristics such as geneti...