Sciweavers

51 search results - page 4 / 11
» Learning to Assign Degrees of Belief in Relational Domains
Sort
View
JAIR
2010
145views more  JAIR 2010»
13 years 5 months ago
Planning with Noisy Probabilistic Relational Rules
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...
Tobias Lang, Marc Toussaint
SOFSEM
2007
Springer
14 years 1 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
AAAI
2011
12 years 7 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
FLAIRS
2007
13 years 9 months ago
Learning to Identify Global Bottlenecks in Constraint Satisfaction Search
Using information from failures to guide subsequent search is an important technique for solving combinatorial problems in domains such as boolean satisfiability (SAT) and constr...
Diarmuid Grimes, Richard J. Wallace
AH
2008
Springer
14 years 1 months ago
A Validation Framework for Formal Models in Adaptive Work-Integrated Learning
The focus of my thesis is on the development of a multi-method framework for the validation of formal models (domain model, user model, and teaching model) for adaptive work-integr...
Barbara Kump