Abstract
The most intriguing and ethically challenging roles of robots in society are those of collaborator and social partner. We propose that such robots must have the capacity to learn, represent, activate, and apply social and moral norms—they must have a norm capacity. We offer a theoretical analysis of two parallel questions: what constitutes this norm capacity in humans and how might we implement it in robots? We propose that the human norm system has four properties: flexible learning despite a general logical format, structured representations, context-sensitive activation, and continuous updating. We explore two possible models that describe how norms are cognitively represented and activated in context-specific ways and draw implications for robotic architectures that would implement either model.
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Notes
- 1.
This is a cognitive definition of a norm, and it allows for an agent to endorse an illusory norm—when all three conditions are met but community members do not in fact follow the instruction and do not in fact expect others to follow the instruction. If we want to model and predict the agent’s behavior, however, we can still consider the person to follow a perceived norm (Aarts and Dijksterhuis 2003).
- 2.
The threshold of sufficiency will typically be a majority but may vary by norm type and community.
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Acknowledgements
This work was in part funded by a grant from the Office of Naval Research (ONR), No. N00014-14-1-0144, and a grant from the Defense Advanced Research Projects Agency (DARPA), DARPA SIMPLEX No. 14-46-FP-097. The opinions expressed here are our own and do not necessarily reflect the views of ONR or DARPA.
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Malle, B.F., Scheutz, M., Austerweil, J.L. (2017). Networks of Social and Moral Norms in Human and Robot Agents. In: Aldinhas Ferreira, M., Silva Sequeira, J., Tokhi, M., E. Kadar, E., Virk, G. (eds) A World with Robots. Intelligent Systems, Control and Automation: Science and Engineering, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-319-46667-5_1
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