Searched for: subject%3A%22planning%255C%252Bunder%255C%252Buncertainty%22
(1 - 4 of 4)
document
Molhoek, Jord (author)
Many real-world problems fall in the category of sequential decision-making under uncertainty; Markov Decision Processes (MDPs) are a common method for modeling such problems. To solve an MDP, one could start from scratch or one could already have an idea of what good policies look like. Furthermore, there could be uncertainty in this idea. In...
master thesis 2024
document
LIU, Xinjie (author)
Many autonomous navigation tasks require mobile robots to operate in dynamic environments involving interactions between agents. Developing interaction-aware motion planning algorithms that enable safe and intelligent interactions remains challenging. Dynamic game theory renders a powerful mathematical framework to model these interactions...
master thesis 2023
document
van Elsas, Maarten (author)
In dynamic scheduling, some of the information about the scheduling problem is learnt during the execution of the schedule. Because of this, creating a low cost schedule that does not have to be changed is impossible in most cases. When changes are made during the execution, focussing solely on the performance of the schedule may result in very...
master thesis 2019
document
van Bekkum, Rob (author)
Decision-theoretic planning techniques are increasingly being used to obtain (optimal) plans for domains involving uncertainty, which may be present in the form of the controlling agent's actions, its percepts, or exogenous factors in the domain. These techniques build on detailed probabilistic models of the underlying system, for which Markov...
master thesis 2017
Searched for: subject%3A%22planning%255C%252Bunder%255C%252Buncertainty%22
(1 - 4 of 4)