3 citations · 7 across the 10 of their papers we have counts for
4 papers · 1 filter
Propose to Learn, Learn to Propose: Evaluability-Aware Assistance under Bounded Rationality
Yifan Zhu, Sammie Katt, Samuel Kaski
AI assistants often collaborate by proposing candidate edits, plans, or designs that users evaluate before adoption. Existing assistance methods focus on proposal quality or user-g…
More Than Irrational: Modeling Belief-Biased Agents
Yifan Zhu, Sammie Katt, Samuel Kaski
Despite the explosive growth of AI and the technologies built upon it, predicting and inferring the sub-optimal behavior of users or human collaborators remains a critical challeng…
Bayesian Reinforcement Learning in Factored POMDPs
Sammie Katt, Frans Oliehoek, Christopher Amato
Bayesian approaches provide a principled solution to the exploration-exploitation trade-off in Reinforcement Learning. Typical approaches, however, either assume a fully observable…
Learning in POMDPs with Monte Carlo Tree Search
Sammie Katt, Frans A. Oliehoek, Christopher Amato
The POMDP is a powerful framework for reasoning under outcome and information uncertainty, but constructing an accurate POMDP model is difficult. Bayes-Adaptive Partially Observabl…