Abstract
We present a new computational framework for modeling moral decision-making in artificial agents based on the notion of ‘Machine Morality as Cooperation’. This framework integrates recent advances from cross-disciplinary moral decision-making literature into a single architecture. We build upon previous work outlining cognitive elements that an artificial agent would need for exhibiting latent morality, and we extend it by providing a computational realization of the cognitive architecture of such an agent. Our work has implications for cognitive and social robotics. Recent studies in human neuroimaging have pointed to three different decision-making processes, Pavlovian, model-free and model-based, that are defined by distinct neural substrates in the brain. Here, we describe how computational models of these three cognitive processes can be implemented in a single cognitive architecture by using the distributed and hierarchical organization proposed by the DAC theoretical framework. Moreover, we propose that a pro-social drive to cooperate exists at the Pavlovian level that can also bias the rest of the decision system, thus extending current state-of-the-art descriptive models based on harm-aversion.
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This research received funding from H2020-EU, ID: 820742.
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Freire, I.T., Urikh, D., Arsiwalla, X.D., Verschure, P.F.M.J. (2020). Machine Morality: From Harm-Avoidance to Human-Robot Cooperation. In: Vouloutsi, V., Mura, A., Tauber, F., Speck, T., Prescott, T.J., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2020. Lecture Notes in Computer Science(), vol 12413. Springer, Cham. https://doi.org/10.1007/978-3-030-64313-3_13
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