249 search results - page 4 / 50 » Learning Causal Models of Relational Domains |
BMCBI
2007
13 years 7 months ago
2007
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
KR
2004
Springer
14 years 21 days 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...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models de...
JCP
2008
13 years 7 months ago
2008
In this paper, we propose a model named Logical Markov Decision Processes with Negation for Relational Reinforcement Learning for applying Reinforcement Learning algorithms on the ...
ISMDA
2001
Springer
13 years 11 months ago
2001
Springer
In recent years, a number of algorithms have been developed for learning the structure of Bayesian networks from data. In this paper we apply some of these algorithms to a realist...