Abstract | ||
---|---|---|
People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration has been less well studied computationally. Here, we develop a Bayesian model for teaching by demonstration. Stark differences arise when demonstrators are intentionally teaching (i.e. showing) a task versus simply performing (i.e. doing) a task. In two experiments, we show that human participants modify their teaching behavior consistent with the predictions of our model. Further, we show that even standard IRL algorithms benefit when learning from showing versus doing. |
Year | Venue | Field |
---|---|---|
2016 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016) | Bayesian inference,Computer science,Inverse reinforcement learning,Artificial intelligence,Machine learning |
DocType | Volume | ISSN |
Conference | 29 | 1049-5258 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Mark Ho | 1 | 15 | 8.42 |
Michael L. Littman | 2 | 9798 | 961.84 |
James MacGlashan | 3 | 19 | 2.90 |
Fiery Cushman | 4 | 14 | 10.88 |
Joseph L. Austerweil | 5 | 119 | 21.60 |