Comparing Reinforcement Learning and Human Learning using the Game of Hidden Rules

no code implementations • 30 Jun 2023 • Eric Pulick, Vladimir Menkov, Yonatan Mintz, Paul Kantor, Vicki Bier

Reliable real-world deployment of reinforcement learning (RL) methods requires a nuanced understanding of their strengths and weaknesses and how they compare to those of humans.

reinforcement-learning Reinforcement Learning (RL)

The Game of Hidden Rules: A New Kind of Benchmark Challenge for Machine Learning

no code implementations • 20 Jul 2022 • Eric Pulick, Shubham Bharti, Yiding Chen, Vladimir Menkov, Yonatan Mintz, Paul Kantor, Vicki M. Bier

Existing benchmark environments for ML, such as board and video games, offer well-defined benchmarks for progress, but constituent tasks are often complex, and it is frequently unclear how task characteristics contribute to overall difficulty for the machine learner.

Ontology alignment: A Content-Based Bayesian Approach

no code implementations • 24 Aug 2019 • Vladimir Menkov, Paul Kantor

The estimates themselves are given by a ``black box'' polytomous logistic regression model (PLRM), and thus can be easily generalized to the case of any arbitrary probability-generating model.

regression