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- extended-abstractMay 2016
Policy Shaping in Domains with Multiple Optimal Policies: (Extended Abstract)
AAMAS '16: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent SystemsPages 1455–1456In many domains, there exist multiple ways for an agent to achieve optimal performance. Feedback may be provided along one or more of them to aid learning. In this work, we investigate whether humans have a preference towards providing feedback along ...
- ArticleJuly 2015
Policy shaping with human teachers
IJCAI'15: Proceedings of the 24th International Conference on Artificial IntelligencePages 3366–3372In this work we evaluate the performance of a policy shaping algorithm using 26 human teachers. We examine if the algorithm is suitable for human-generated data on two different boards in a pac-man domain, comparing performance to an oracle that ...
- research-articleSeptember 2013
From Language to Motor Gavagai: Unified Imitation Learning of Multiple Linguistic and Nonlinguistic Sensorimotor Skills
IEEE Transactions on Autonomous Mental Development (ITAMD), Volume 5, Issue 3Pages 222–239https://doi.org/10.1109/TAMD.2013.2279277We identify a strong structural similarity between the Gavagai problem in language acquisition and the problem of imitation learning of multiple context-dependent sensorimotor skills from human teachers. In both cases, a learner has to resolve ...
- ArticleSeptember 2009
Combining different interaction strategies reduces uncertainty when bootstrapping a lexicon
When bootstrapping a new language, the agents in a population need to be able to agree on the meaning of the individual words. In order to do so, they need to overcome the problem of referential uncertainty, which captures the idea that the meaning of ...