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2017
Sarathy, V.; Scheutz, M.; Malle, B. F. (2017) : Learning behavioral norms in uncertain and changing contexts: 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom): Debrecen, Hungary: IEEE, S. 000301-000306

DOI: https://doi.org/10.1109/CogInfoCom.2017.8268261

Abstract: Human behavior is often guided by social and moral norms. Robots that enter human societies must therefore behave in norm-conforming ways as well to increase coordination, predictability, and safety in human-robot interactions. However, human norms are context-specific and laced with uncertainty, making the representation, learning, and communication of norms challenging. We provide a formal representation of norms using deontic logic, Dempster-Shafer Theory, and a machine learning algorithm that allows an artificial agent to learn norms under uncertainty from human data. We demonstrate a novel cognitive capability with which an agent can dynamically learn norms while being exposed to distinct contexts, recognizing the unique identity of each context and the norms that apply in it.

Keywords: Artificial agent, behavioral norms, behavioural sciences computing, Cognition, Conferences, Dempster-Shafer Theory, deontic logic, Ethics, formal logic, Human behavior, human societies, human-robot interaction, human-robot interactions, ieee xplore, inference mechanisms, learning (artificial intelligence), Libraries, machine learning algorithm, Moral & Ethik, moral norms, norm-conforming ways, Robot kinematics, Social Norms, uncertainty, uncertainty handling

2017

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