Add to ORKGPlanning under uncertainty is a central problem in developing intelligent autonomous sys-tems. 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 un-certainty 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.
When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) f...
AbstractMany traditional solution approaches to relationally specified decision-theoretic planning p...
Markov decision processes (MDP) offer a rich model that has been extensively used by the AI communit...
Planning under uncertainty is a central problem in developing intelligent autonomous systems. The tr...
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 mechanism for planning under uncertainty. However,...
Markov Decision Problems, MDPs offer an effective mech-anism for planning under uncertainty. However...
We consider stochastic planning problems that involve mul-tiple objectives such as minimizing task c...
Reasoning about uncertainty is an essential component of many real-world plan-ning problems, such as...
Abstract. This paper proposes an unifying formulation for nondeter-ministic and probabilistic planni...
While MDPs are powerful tools for modeling sequential decision making problems under uncertainty, th...
While Markov Decision Processes (MDPs) have been shown to be effective models for planning under unc...
Broadly speaking, I am interested in sequential decision-making under uncertainty, and optimal meth-...
When modeling real-world decision-theoretic planning problems in the Markov decision process (MDP) f...
When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) f...
AbstractMany traditional solution approaches to relationally specified decision-theoretic planning p...
Markov decision processes (MDP) offer a rich model that has been extensively used by the AI communit...
Planning under uncertainty is a central problem in developing intelligent autonomous systems. The tr...
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 mechanism for planning under uncertainty. However,...
Markov Decision Problems, MDPs offer an effective mech-anism for planning under uncertainty. However...
We consider stochastic planning problems that involve mul-tiple objectives such as minimizing task c...
Reasoning about uncertainty is an essential component of many real-world plan-ning problems, such as...
Abstract. This paper proposes an unifying formulation for nondeter-ministic and probabilistic planni...
While MDPs are powerful tools for modeling sequential decision making problems under uncertainty, th...
While Markov Decision Processes (MDPs) have been shown to be effective models for planning under unc...
Broadly speaking, I am interested in sequential decision-making under uncertainty, and optimal meth-...
When modeling real-world decision-theoretic planning problems in the Markov decision process (MDP) f...
When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) f...
AbstractMany traditional solution approaches to relationally specified decision-theoretic planning p...
Markov decision processes (MDP) offer a rich model that has been extensively used by the AI communit...