Add to ORKGPlanning under uncertainty is a central problem in developing intelligent autonomous systems. The traditional representation for these problems is a Markov Decision Process (MDP). The MDP model can be extended to a Multi-criteria MDP (MMDP) for planning under uncertainty while trying to optimize multiple criteria. However, due to the trade-offs involved in multi criteria problems there may be infinitely many optimal solutions. The focus of this project has been to find a method that efficiently computes a subset of solutions that represents the entire set of optimal solutions for bi-objective MDPs
Markov decision processes (MDP) offer a rich model that has been extensively used by the AI communit...
Iteratively solving a set of linear programs (LPs) is a common strategy for solving various decision...
The parameters for a Markov Decision Process (MDP) often cannot be specified exactly. Uncertain MDPs...
Planning under uncertainty is a central problem in developing intelligent autonomous sys-tems. The t...
Thesis (Ph.D.)--University of Washington, 2013The ability to plan in the presence of uncertainty abo...
Markov Decision Problems, MDPs offer an effective mechanism for planning under uncertainty. However,...
Markov Decision Problems, MDPs offer an effective mech-anism for planning under uncertainty. However...
Planning, namely the ability of an autonomous agent to make decisions leading towards a certain goal...
Markov Decision Problems, MDPs offer an effective mechanism for planning under uncertainty. However,...
We consider stochastic planning problems that involve mul-tiple objectives such as minimizing task c...
Decision-theoretic systems, such as Markov Decision Processes (MDPs), are used for sequential decisi...
National Research Foundation (NRF) Singapore under Singapore-MIT Alliance for Research and Technolog...
Decision-makers are often faced with multi-faceted problems that require making trade-offs between m...
This paper addresses the problem of approximating the set of all solutions for Multi-objective Marko...
Reasoning about uncertainty is an essential component of many real-world plan-ning problems, such as...
Markov decision processes (MDP) offer a rich model that has been extensively used by the AI communit...
Iteratively solving a set of linear programs (LPs) is a common strategy for solving various decision...
The parameters for a Markov Decision Process (MDP) often cannot be specified exactly. Uncertain MDPs...
Planning under uncertainty is a central problem in developing intelligent autonomous sys-tems. The t...
Thesis (Ph.D.)--University of Washington, 2013The ability to plan in the presence of uncertainty abo...
Markov Decision Problems, MDPs offer an effective mechanism for planning under uncertainty. However,...
Markov Decision Problems, MDPs offer an effective mech-anism for planning under uncertainty. However...
Planning, namely the ability of an autonomous agent to make decisions leading towards a certain goal...
Markov Decision Problems, MDPs offer an effective mechanism for planning under uncertainty. However,...
We consider stochastic planning problems that involve mul-tiple objectives such as minimizing task c...
Decision-theoretic systems, such as Markov Decision Processes (MDPs), are used for sequential decisi...
National Research Foundation (NRF) Singapore under Singapore-MIT Alliance for Research and Technolog...
Decision-makers are often faced with multi-faceted problems that require making trade-offs between m...
This paper addresses the problem of approximating the set of all solutions for Multi-objective Marko...
Reasoning about uncertainty is an essential component of many real-world plan-ning problems, such as...
Markov decision processes (MDP) offer a rich model that has been extensively used by the AI communit...
Iteratively solving a set of linear programs (LPs) is a common strategy for solving various decision...
The parameters for a Markov Decision Process (MDP) often cannot be specified exactly. Uncertain MDPs...