73 search results - page 1 / 15 » Incremental Learning of Planning Operators in Stochastic Dom... |
SOFSEM
2007
Springer
13 years 11 months ago
2007
Springer
In this work we assume that there is an agent in an unknown environment (domain). This agent has some predefined actions and it can perceive its current state in the environment c...
ICML
1995
IEEE
14 years 5 months ago
1995
IEEE
This paper describes an approach to automatically learn planning operators by observing expert solution traces and to further refine the operators through practice in a learning-b...
JAIR
2007
13 years 4 months ago
2007
In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a a new probabilistic planning rule representation to compactly ...
AIPS
2006
13 years 6 months ago
2006
We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
KR
2004
Springer
13 years 10 months ago
2004
Springer
To 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 probabilist...