Name
Abstract | ||
|---|---|---|
Agents that operate in human environments will need to be able to learn new skills from everyday people. Learning from demonstration (LfD) is a popular paradigm for this. Drawing from our interest in Socially Guided Machine Learning, we explore the impact of interactivity on learning from demonstration. We present findings from a study with human subjects showing people who are able to interact with the learning agent provide better demonstrations (in part) by adapting based on learner performance which results in improved learning performance. We also find that interactivity increases a sense of engagement and may encourage players to participate longer. Our exploration of interactivity sheds light on how best to obtain demonstrations for LfD applications. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/DEVLRN.2010.5578841 | Development and Learning |
Keywords | Field | DocType |
learning by example,batch learning,interactive learning,interactivity,learning agent,socially guided machine learning,games,interviews,machine learning,trajectory | Experiential learning,Interactivity,Interactive Learning,Learning agent,Computer science,Learning from demonstration,Artificial intelligence,Learning by example,Error-driven learning,Multimedia,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-6900-0 | 4 | 0.51 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Zang | 1 | 4 | 0.51 |
| Runhe Tian | 2 | 4 | 0.51 |
| Andrea Lockerd Thomaz | 3 | 1115 | 84.85 |
| Charles L. Isbell | 4 | 504 | 65.79 |