Social interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This thesis provides several contributions towards the understanding of this Socially Guided Machine Learning scenario.
While the topic of human input to machine learning algorithms has been explored to some extent, prior works have not gone far enough to understand what people will try to communicate when teaching a machine and how algorithms and learning systems can be modified to better accommodate a human partner. Interface techniques have been based on intuition and assumptions rather than grounded in human behavior, and often techniques are not demonstrated or evaluated with everyday people.
Using a computer game, Sophie's Kitchen, an experiment with human subjects provides several insights about how people approach the task of teaching a machine. In particular, people want to direct and guide an agent's exploration process, they quickly use the behavior of the agent to infer a mental model of the learning process, and they utilize positive and negative feedback in asymmetric ways. Using a robotic platform, Leonardo, and 200 people in follow-up studies of modified versions of the Sophie's Kitchen game, four research themes are developed.
The use of human guidance in a machine learning exploration can be successfully incorporated to improve learning performance. Novel learning approaches demonstrate aspects of goal-oriented learning. The transparency of the machine learner can have significant effects on the nature of the instruction received from the human teacher, which in turn positively impacts the learning process. Utilizing asymmetric interpretations of positive and negative feedback from a human partner, can result in a more efficient and robust learning experience. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Cited By
- Thomaz A, Cakmak M, Chao C, DePalma N and Gielniak M Interactive robot task learning CHI '10 Extended Abstracts on Human Factors in Computing Systems, (3037-3040)
- Jacobsson H, Hawes N, Kruijff G and Wyatt J Crossmodal content binding in information-processing architectures Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, (81-88)
- Barraquand R and Crowley J Learning polite behavior with situation models Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, (209-216)
- Chernova S and Veloso M Teaching multi-robot coordination using demonstration of communication and state sharing Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3, (1183-1186)
Index Terms
- Socially guided machine learning
Recommendations
Machine Learning: The State of the Art
The two fundamental problems in machine learning (ML) are statistical analysis and algorithm design. The former tells us the principles of the mathematical models that we establish from the observation data. The latter defines the conditions on which ...
Towards human-guided machine learning
IUI '19: Proceedings of the 24th International Conference on Intelligent User InterfacesAutomated Machine Learning (AutoML) systems are emerging that automatically search for possible solutions from a large space of possible kinds of models. Although fully automated machine learning is appropriate for many applications, users often have ...