Planning under uncertainty has been well studied, but usually the uncertainty is in action outcomes. This work instead investigates uncertainty in the amount of time that actions ...
The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous....
Sumeetpal S. Singh, Nikolaos Kantas, Ba-Ngu Vo, Ar...
This paper focuses on the design of control strategies for Evolutionary Algorithms. We propose a method to encapsulate multiple parameters, reducing control to only one criterion. ...
Greedy approximation algorithms have been frequently used to obtain sparse solutions to learning problems. In this paper, we present a general greedy algorithm for solving a class...
We study the problem of projecting a distribution onto (or finding a maximum likelihood distribution among) Markov networks of bounded tree-width. By casting it as the combinatori...