74 search results - page 1 / 15 » Discovering Relational Domain Features for Probabilistic Pla... |
AIPS
2007
13 years 6 months ago
2007
In sequential decision-making problems formulated as Markov decision processes, state-value function approximation using domain features is a critical technique for scaling up the...
JAIR
2010
13 years 2 months ago
2010
Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provide...
KR
2004
Springer
13 years 9 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...
PKDD
2009
Springer
13 years 11 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...
ICDM
2010
IEEE
13 years 2 months ago
2010
IEEE
An important problem that arises during the data mining process in many new emerging application domains is mining data with temporal dependencies. One such application domain is a...