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HRI
2007
ACM
15 years 8 months ago
Learning by demonstration with critique from a human teacher
Learning by demonstration can be a powerful and natural tool for developing robot control policies. That is, instead of tedious hand-coding, a robot may learn a control policy by ...
Brenna Argall, Brett Browning, Manuela M. Veloso
GECCO
2009
Springer
188views Optimization» more  GECCO 2009»
15 years 8 months ago
Exploiting multiple classifier types with active learning
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
Zhenyu Lu, Josh Bongard
ATAL
2006
Springer
15 years 8 months ago
Learning trust strategies in reputation exchange networks
An agent's trust decision strategy consists of the agent's policies for making trust-related decisions, such as who to trust, how trustworthy to be, what reputations to ...
Karen Fullam, K. Suzanne Barber
ATAL
2008
Springer
15 years 6 months ago
Artificial agents learning human fairness
Recent advances in technology allow multi-agent systems to be deployed in cooperation with or as a service for humans. Typically, those systems are designed assuming individually ...
Steven de Jong, Karl Tuyls, Katja Verbeeck
NIPS
2004
15 years 6 months ago
New Criteria and a New Algorithm for Learning in Multi-Agent Systems
We propose a new set of criteria for learning algorithms in multi-agent systems, one that is more stringent and (we argue) better justified than previous proposed criteria. Our cr...
Rob Powers, Yoav Shoham