Add to ORKGWe describe a planner that participates in the Probabilistic Planning Track of the 2004 International Planning Competition. Our planner integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. Forward search from a start state, guided by an admissible heuristic, is used to avoid evaluating all states
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
Markov decision processes (MDP) offer a rich model that has been extensively used by the AI communit...
When planning in an uncertain environment, one is often interested in finding a contingent plan that...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
We describe the version of the GPT planner used in the probabilistic track of the 4th International ...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
We investigate the use Markov Decision Processes a.s a means of representing worlds in which action...
Search algorithms such as LAO* and LRTDP coupled with admissible heuristics are widely used methods ...
We describe the version of the GPT planner to be used in the planning competition. This version, cal...
AbstractWe define the probabilistic planning problem in terms of a probability distribution over ini...
AbstractPlanning graphs have been shown to be a rich source of heuristic information for many kinds ...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
Um dos modelos mais usados para descrever problemas de planejamento probabilístico, i.e., planejamen...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
Markov decision processes (MDP) offer a rich model that has been extensively used by the AI communit...
When planning in an uncertain environment, one is often interested in finding a contingent plan that...
We describe a planning algorithm that integrates two approaches to solving Markov decision processe...
We describe a planning algorithm that integrates two approaches to solving Markov decision processes...
We describe a planning algorithm that integrates two ap-proaches to solving Markov decision processe...
We describe the version of the GPT planner used in the probabilistic track of the 4th International ...
We show how to use symbolic model-checking techniques in heuristic search algorithms for both deter...
We investigate the use Markov Decision Processes a.s a means of representing worlds in which action...
Search algorithms such as LAO* and LRTDP coupled with admissible heuristics are widely used methods ...
We describe the version of the GPT planner to be used in the planning competition. This version, cal...
AbstractWe define the probabilistic planning problem in terms of a probability distribution over ini...
AbstractPlanning graphs have been shown to be a rich source of heuristic information for many kinds ...
A promising approach to solving large state-space search problems is to integrate heuristic search w...
Um dos modelos mais usados para descrever problemas de planejamento probabilístico, i.e., planejamen...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
Markov decision processes (MDP) offer a rich model that has been extensively used by the AI communit...
When planning in an uncertain environment, one is often interested in finding a contingent plan that...