93 search results - page 12 / 19 » Learning action models for multi-agent planning |
ROBOCUP
2007
Springer
14 years 1 months ago
2007
Springer
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
UAI
2008
13 years 9 months ago
2008
We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...
JAIR
2010
13 years 6 months ago
2010
Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually in...
PKDD
2009
Springer
14 years 2 months ago
2009
Springer
Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that gr...
DAGSTUHL
2007
13 years 9 months ago
2007
The ways in which an agent’s actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of ...