249 search results - page 7 / 50 » Learning Causal Models of Relational Domains |
ECAI
2010
Springer
13 years 4 months ago
2010
Springer
Abstract. Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects...
PKDD
2009
Springer
14 years 1 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...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Nonlinear ICA may not result in nonlinear blind source separation, since solutions to nonlinear ICA are highly non-unique. In practice, the nonlinearity in the data generation pro...
CONNECTION
2006
13 years 7 months ago
2006
By learning a range of possible times over which the effect of an action can take place, a robot can reason more effectively about causal and contingent relationships in the world...
ATAL
2009
Springer
14 years 1 months ago
2009
Springer
Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...