Teaching Games : Statistical Sampling Assumptions for Learning in Pedagogical Situations

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “…to compare, and (3) alternate training methods (e.g., Goyal et al., 2016) in which the same system is trained on different data sets to understand how it is using information; providing counter-examples (e.g., Shafto and Goodman, 2008; Shafto, Goodman, and Griffiths, 2014; Nguyen et al., 2015).”
Machine Teaching for Bayesian Learners in the Exponential Family (2013, methodology): “An interesting variant studied in cognitive science is when the learner expects to be taught [20, 8].”
Children's imitation of causal action sequences is influenced by statistical and pedagogical evidence. (2011, background): “…believes the demonstrator is a helpful teacher, then they could expect the teacher to choose their actions, p(ajh), with the goal of having the learner infer the correct hypothesis, p t ðajhÞ / p l ðhja; eÞ ð 6Þ where t and l indicate teacher and learner, respectively (Shafto & Goodman, 2008).”
Algorithmic and Human Teaching of Sequential Decision Tasks (2012, background): “Pedagogical sampling assumes a benevolent teacher and learner; consequently the teacher chooses most informative examples for the learner (Shafto and Goodman 2008; Warner, Stoess, and Shafto 2011).”