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Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

de Greeff J, Belpaeme T - PLoS ONE (2015)

Bottom Line: We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot.In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive.This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

View Article: PubMed Central - PubMed

Affiliation: Centre for Robotics and Neural Systems, Plymouth University, Plymouth, United Kingdom; Interactive Intelligence Group, Delft University of Technology, Delft, the Netherlands.

ABSTRACT
Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

No MeSH data available.


Overview of the experimental set-up, showing the LightHead robot, the touchscreen used to play an interactive learning game and the participant.The individual in this image has given written informed consent (as outlined in PLOS consent form) to publish these case details.
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pone.0138061.g003: Overview of the experimental set-up, showing the LightHead robot, the touchscreen used to play an interactive learning game and the participant.The individual in this image has given written informed consent (as outlined in PLOS consent form) to publish these case details.

Mentions: During the game, participants were seated across the robot, with the touchscreen in between that displayed images of animals and seven potential category labels (Fig 3 [50]). The animals were drawn from the Zoo dataset (UCI Machine Learning Repository [51]), which contains 100 animal exemplars belonging to 7 different categories. Animals are encoded through 15 Boolean-valued attributes such as ‘hair’, ‘feathers’, ‘aquatic’ etc. and 1 numerical-valued attribute ‘number of legs’. For each of the animals, an image (found through Google image search) was displayed and both the robot and the participant were shown these images during the experiment.


Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

de Greeff J, Belpaeme T - PLoS ONE (2015)

Overview of the experimental set-up, showing the LightHead robot, the touchscreen used to play an interactive learning game and the participant.The individual in this image has given written informed consent (as outlined in PLOS consent form) to publish these case details.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4589374&req=5

pone.0138061.g003: Overview of the experimental set-up, showing the LightHead robot, the touchscreen used to play an interactive learning game and the participant.The individual in this image has given written informed consent (as outlined in PLOS consent form) to publish these case details.
Mentions: During the game, participants were seated across the robot, with the touchscreen in between that displayed images of animals and seven potential category labels (Fig 3 [50]). The animals were drawn from the Zoo dataset (UCI Machine Learning Repository [51]), which contains 100 animal exemplars belonging to 7 different categories. Animals are encoded through 15 Boolean-valued attributes such as ‘hair’, ‘feathers’, ‘aquatic’ etc. and 1 numerical-valued attribute ‘number of legs’. For each of the animals, an image (found through Google image search) was displayed and both the robot and the participant were shown these images during the experiment.

Bottom Line: We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot.In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive.This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

View Article: PubMed Central - PubMed

Affiliation: Centre for Robotics and Neural Systems, Plymouth University, Plymouth, United Kingdom; Interactive Intelligence Group, Delft University of Technology, Delft, the Netherlands.

ABSTRACT
Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

No MeSH data available.