39 search results - page 1 / 8 » Learning Probabilistic Relational Planning Rules |
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...
DAGSTUHL
2007
13 years 6 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 ...
JAIR
2010
13 years 3 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...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
Inference in graphical models has emerged as a promising technique for planning. A recent approach to decision-theoretic planning in relational domains uses forward inference in d...