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NEUROSCIENCE
2001
Springer
15 years 10 months ago
Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...
Doina Caragea, Adrian Silvescu, Vasant Honavar
CHI
1999
ACM
15 years 10 months ago
Learning and Performing by Exploration: Label Quality Measured by Latent Semantic Analysis
Models of learning and performing by exploration assume that the semantic distance between task descriptions and screen labels controls in part the usersÕ search strategies. Neve...
Rodolfo Soto
WWW
2005
ACM
16 years 6 months ago
Towards autonomic web-sites based on learning automata
Autonomics or self-reorganization becomes pertinent for websites serving a large number of users with highly varying workloads. An important component of self-adaptation is to mod...
Pradeep S, Chitra Ramachandran, Srinath Srinivasa
IJCAI
2007
15 years 7 months ago
A Theoretical Framework for Learning Bayesian Networks with Parameter Inequality Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
ATAL
2006
Springer
15 years 9 months ago
Rule value reinforcement learning for cognitive agents
RVRL (Rule Value Reinforcement Learning) is a new algorithm which extends an existing learning framework that models the environment of a situated agent using a probabilistic rule...
Christopher Child, Kostas Stathis