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EWLR
1997
Springer
15 years 8 months ago
Learning Complex Robot Behaviours by Evolutionary Computing with Task Decomposition
Building robots can be a tough job because the designer has to predict the interactions between the robot and the environment as well as to deal with them. One solution to cope the...
Wei-Po Lee, John Hallam, Henrik Hautop Lund
ACMICEC
2006
ACM
110views ECommerce» more  ACMICEC 2006»
15 years 8 months ago
Learning inventory management strategies for commodity supply chains with customer satisfaction
In this paper, we look at a supply chain of commodity goods where customer demand is uncertain and partly based on reputation, and where raw material replenishment is uncertain in...
Jeroen van Luin, Han La Poutré, J. Will M. ...
ECAI
2008
Springer
15 years 6 months ago
Belief revision with reinforcement learning for interactive object recognition
From a conceptual point of view, belief revision and learning are quite similar. Both methods change the belief state of an intelligent agent by processing incoming information. Ho...
Thomas Leopold, Gabriele Kern-Isberner, Gabriele P...
ECIS
2001
15 years 5 months ago
Connecting Knowledge Management and Experiential Learning to Gain New Insights and Research Perspectives
There is a growing sense that knowledge management, to be most effective, must address questions of knowledge creation and innovation in organisational contexts. Therefore, knowle...
Jordi Comas, Sandra Sieber
IJCAI
2001
15 years 5 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz