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...
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...
—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...
Designing distributed controllers for self-reconfiguring modular robots has been consistently challenging. We have developed a reinforcement learning approach which can be used bo...
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...