Sciweavers

Share
41 search results - page 3 / 9
» Learning Probabilistic Relational Planning Rules
Sort
View
PKDD
2009
Springer
102views Data Mining» more  PKDD 2009»
9 years 8 months ago
Relevance Grounding for Planning in Relational Domains
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...
Tobias Lang, Marc Toussaint
SOFSEM
2007
Springer
9 years 7 months ago
Incremental Learning of Planning Operators in Stochastic Domains
In this work we assume that there is an agent in an unknown environment (domain). This agent has some predefined actions and it can perceive its current state in the environment c...
Javad Safaei, Gholamreza Ghassem-Sani
JAIR
2010
131views more  JAIR 2010»
9 years 1 days ago
Automatic Induction of Bellman-Error Features for Probabilistic Planning
Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provide...
Jia-Hong Wu, Robert Givan
ICML
2005
IEEE
10 years 2 months ago
Learning first-order probabilistic models with combining rules
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...
Sriraam Natarajan, Prasad Tadepalli, Eric Altendor...
SDM
2010
SIAM
158views Data Mining» more  SDM 2010»
9 years 3 months ago
On the Use of Combining Rules in Relational Probability Trees
A relational probability tree (RPT) is a type of decision tree that can be used for probabilistic classification of instances with a relational structure. Each leaf of an RPT cont...
Daan Fierens
books