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Found 5 papers, 0 papers with code
Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrations
To this end, we propose a novel inverse reinforcement learning (IRL) method for learning implicit hard and soft constraints from demonstrations, enabling agents to quickly adapt to new settings.
Modeling Voters in Multi-Winner Approval Voting
In multi-winner approval voting (AV), an agent submits a ballot consisting of approvals for as many candidates as they wish, and winners are chosen by tallying up the votes and choosing the top-$k$ candidates receiving the most approvals.
Heuristic Strategies in Uncertain Approval Voting Environments
In real world voting scenarios, people often do not have complete information about other voter preferences and it can be computationally complex to identify a strategy that will maximize their expected utility.
Heuristics in Multi-Winner Approval Voting
In multi-winner approval voting (AV), an agent may vote for as many candidates as they wish.
CPMetric: Deep Siamese Networks for Learning Distances Between Structured Preferences
CPDist is a novel metric learning approach based on the use of deep siamese networks which learn the Kendal Tau distance between partial orders that are induced by compact preference representations.
