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SIGECOM
2004
ACM
135views ECommerce» more  SIGECOM 2004»
16 years 21 days ago
Applying learning algorithms to preference elicitation
We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory. We show that l...
Sébastien Lahaie, David C. Parkes
ECAI
2004
Springer
16 years 20 days ago
A Backtracking Strategy for Order-Independent Incremental Learning
Agents that exist in an environment that changes over time, and are able to take into account the temporal nature of experience, are commonly called incremental learners. It is wid...
Nicola Di Mauro, Floriana Esposito, Stefano Ferill...
AR
2007
105views more  AR 2007»
15 years 7 months ago
Reinforcement learning of a continuous motor sequence with hidden states
—Reinforcement learning is the scheme for unsupervised learning in which robots are expected to acquire behavior skills through self-explorations based on reward signals. There a...
Hiroaki Arie, Tetsuya Ogata, Jun Tani, Shigeki Sug...
IJRR
2008
186views more  IJRR 2008»
15 years 7 months ago
Automated Design of Adaptive Controllers for Modular Robots using Reinforcement Learning
Designing distributed controllers for self-reconfiguring modular robots has been consistently challenging. We have developed a reinforcement learning approach which can be used bo...
Paulina Varshavskaya, Leslie Pack Kaelbling, Danie...
AAI
2005
138views more  AAI 2005»
15 years 7 months ago
Learning By Teaching: A New Agent Paradigm For Educational Software
This paper discusses Betty's Brain, a teachable agent in the domain of river ecosystems that combines learning by teaching with self-regulation mentoring to promote deep lear...
Gautam Biswas, Krittaya Leelawong, Daniel Schwartz...