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FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation
Preference-based learning aims to align robot task objectives with human values.
Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making
Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers.
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
To mitigate this gap, we proposed to promote humans' appropriate trust based on the CL of both sides at a task-instance level.
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning
Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers.
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