Add to ORKGTo learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPS-like planning operators from examples. We demonstrate the effective learning of rule-based operators for a wide range of traditional planning domains
There have been great advances in the probabilistic planning community during recent years, and plan...
Algorithms for planning under uncertainty require accurate action models that explicitly capture the...
In this work we propose an on-line learning method for learning action rules for planning. The syste...
Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. ...
Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. ...
In this article, we work towards the goal of developing agents that can learn to act in complex worl...
Using machine learning techniques for planning is getting increasingly more important in recent year...
Using machine learning techniques for planning is getting in-creasingly more important in recent yea...
Probabilistic planners have improved recently to the point that they can solve difficult tasks with ...
In sequential decision-making problems formulated as Markov decision processes, state-value function...
We consider how to learn useful relational features in lin-ear approximated value function represent...
In sequential decision-making problems formulated as Markov decision processes, state-value function...
International audienceProbabilistic planners have improved recently to the point that they can solve...
The ways in which an agent\u27s actions affect the world can often be modeled compactly using a set ...
Probabilistic planners are very flexible tools that provide good solutions for difficult tasks. Howe...
There have been great advances in the probabilistic planning community during recent years, and plan...
Algorithms for planning under uncertainty require accurate action models that explicitly capture the...
In this work we propose an on-line learning method for learning action rules for planning. The syste...
Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. ...
Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. ...
In this article, we work towards the goal of developing agents that can learn to act in complex worl...
Using machine learning techniques for planning is getting increasingly more important in recent year...
Using machine learning techniques for planning is getting in-creasingly more important in recent yea...
Probabilistic planners have improved recently to the point that they can solve difficult tasks with ...
In sequential decision-making problems formulated as Markov decision processes, state-value function...
We consider how to learn useful relational features in lin-ear approximated value function represent...
In sequential decision-making problems formulated as Markov decision processes, state-value function...
International audienceProbabilistic planners have improved recently to the point that they can solve...
The ways in which an agent\u27s actions affect the world can often be modeled compactly using a set ...
Probabilistic planners are very flexible tools that provide good solutions for difficult tasks. Howe...
There have been great advances in the probabilistic planning community during recent years, and plan...
Algorithms for planning under uncertainty require accurate action models that explicitly capture the...
In this work we propose an on-line learning method for learning action rules for planning. The syste...