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Moral Gridworlds: A Theoretical Proposal for Modeling Artificial Moral Cognition

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Abstract

I describe a suite of reinforcement learning environments in which artificial agents learn to value and respond to moral content and contexts. I illustrate the core principles of the framework by characterizing one such environment, or “gridworld,” in which an agent learns to trade-off between monetary profit and fair dealing, as applied in a standard behavioral economic paradigm. I then highlight the core technical and philosophical advantages of the learning approach for modeling moral cognition, and for addressing the so-called value alignment problem in AI.

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Notes

  1. The Ultimatum Game appears to track fairness. This does not yet mean that it explains fairness, i.e., that it provides an account of ‘how fairness works.’ However, to model fairness, the fairness gridworld only needs such a benchmark of human behavior, not a full-fledged or mechanistic explanation of it. On the contrary, the gridworld may be one way to begin looking inside the ‘black box’ of fairness (for more on this last idea, see Sect. 3.2) Thank you to an anonymous reviewer to pressing me on this point.

  2. This is also known as an indefinite horizon task, i.e., an interaction which lasts an indefinite period of time, but eventually terminates.

  3. Thanks to Ivan Gonzalez-Cabrera for suggesting this point.

  4. Interest in modeling a three-way relation between monetary value, fairness, and honesty considerations may further weigh in favor of a MORL rather than a specification approach (see Sect. 2.3).

  5. Although its implementation of a consequentialist ethics (and, specifically, Asimov’s three laws for governing robotic behavior) technically makes the proposal a model of normative moral AI, the paper’s heavy emphasis on modeling the naturalistic simulation theory of cognition lends to many of the objectives of what I am calling the (descriptive) moral psychological approach.

  6. Thanks to an anonymous reviewer for pressing me on this point. For further discussion concerning the difficulties of context identification in machine ethics, see Winfield et al. (2019).

References

  • Adamson, G., Havens, J. C., & Chatila, R. (2019). Designing a value-driven future for ethical autonomous and intelligent systems. Proceedings of the IEEE,107(3), 518–525.

    Google Scholar 

  • Allen, C., Smit, I., & Wallach, W. (2005). Artificial morality: Top-down, bottom-up, and hybrid approaches. Ethics and Information Technology,7(3), 149–155.

    Google Scholar 

  • Allen, C., & Wallach, W. (2012). Moral machines: Contradiction in terms or abdication of human responsibility. In Robot ethics: The ethical and social implications of robotics (p. 55–68). Cambridge: MIT Press.

  • Alvard, M. S. (2004). The ultimatum game, fairness, and cooperation among big game hunters. In J. Henrich, R. Boyd, S. Bowles, C. Camerer, E. Fehr, & H. Gintis (Eds.), Foundations of human sociality (pp. 413–435). Oxford: Oxford University Press.

    Google Scholar 

  • Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.

  • Anderson, M., & Anderson, S. L. (2018). GenEth: a general ethical dilemma analyzer. Paladyn, Journal of Behavioral Robotics,9(1), 337–357.

    Google Scholar 

  • Anderson, M., Anderson, S. L., & Armen, C. (2006). MedEthEx: a prototype medical ethics advisor. In Proceedings of the national conference on artificial intelligence (Vol. 21, No. 2, p. 1759). Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999.

  • Anderson, M., Anderson, S. L., & Berenz, V. (2019). A value-driven eldercare robot: Virtual and physical instantiations of a case-supported principle-based behavior paradigm. Proceedings of the IEEE,107(3), 526–540.

    Google Scholar 

  • Arnold, T., Kasenberg, D., & Scheutz, M. (2017). Value alignment or misalignment—What will keep systems accountable?. In Workshops at the thirty-first AAAI conference on artificial intelligence.

  • Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review,104, 671.

    Google Scholar 

  • Bechtel, W., & Mundale, J. (1999). Multiple realizability revisited: Linking cognitive and neural states. Philosophy of Science,66(2), 175–207.

    Google Scholar 

  • Bengio, Y., & LeCun, Y. (2007). Scaling learning algorithms towards AI. Large-scale Kernel Machines,34(5), 1–41.

    Google Scholar 

  • Berns, G. S., Bell, E., Capra, C. M., Prietula, M. J., Moore, S., Anderson, B., et al. (2012). The price of your soul: Neural evidence for the non-utilitarian representation of sacred values. Philosophical Transactions of the Royal Society B: Biological Sciences,367(1589), 754–762.

    Google Scholar 

  • Bigman, Y. E., Waytz, A., Alterovitz, R., & Gray, K. (2019). Holding robots responsible: The elements of machine morality. Trends in Cognitive Sciences,23(5), 365–368.

    Google Scholar 

  • Boksem, M. A., & De Cremer, D. (2010). Fairness concerns predict medial frontal negativity amplitude in ultimatum bargaining. Social Neuroscience,5(1), 118–128.

    Google Scholar 

  • Bonnefon, J. F., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science,352(6293), 1573–1576.

    Google Scholar 

  • Borenstein, J., & Arkin, R. (2019). Robots, ethics, and intimacy: the need for scientific research. In D. Berkich & M. V. d’Alfonso (Eds.), On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence (Vol. 134, pp. 299–309). Springer.

  • Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement learning, fast and slow. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2019.02.006.

    Article  Google Scholar 

  • Bremner, P., Dennis, L. A., Fisher, M., & Winfield, A. F. (2019). On proactive, transparent, and verifiable ethical reasoning for robots. Proceedings of the IEEE,107(3), 541–561.

    Google Scholar 

  • Brown, D. (1991). Human universals. New York: McGraw-Hill.

    Google Scholar 

  • Brumbaugh, S. M., Sanchez, L. A., Nock, S. L., & Wright, J. D. (2008). Attitudes toward gay marriage in states undergoing marriage law transformation. Journal of Marriage and Family,70(2), 345–359.

    Google Scholar 

  • Cave, S., Nyrup, R., Vold, K., & Weller, A. (2018). Motivations and risks of machine ethics. Proceedings of the IEEE,107(3), 562–574.

    Google Scholar 

  • Cervantes, J. A., López, S., Rodríguez, L. F., Cervantes, S., Cervantes, F., & Ramos, F. (2019). Artificial moral agents: A survey of the current status. Science and Engineering Ethics. https://doi.org/10.1007/s11948-019-00151-x.

    Article  Google Scholar 

  • Corradi-Dell’Acqua, C., Civai, C., Rumiati, R. I., & Fink, G. R. (2013). Disentangling self-and fairness-related neural mechanisms involved in the ultimatum game: an fMRI study. Social Cognitive and Affective Neuroscience,8(4), 424–431.

    Google Scholar 

  • Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature,538(7625), 311–313.

    Google Scholar 

  • Crockett, M. J. (2013). Models of morality. Trends in Cognitive Sciences,17(8), 363–366.

    Google Scholar 

  • Crockett, M. J. (2016). How formal models can illuminate mechanisms of moral judgment and decision making. Current Directions in Psychological Science,25(2), 85–90.

    Google Scholar 

  • Crockett, M. J., Siegel, J. Z., Kurth-Nelson, Z., Dayan, P., & Dolan, R. J. (2017). Moral transgressions corrupt neural representations of value. Nature Neuroscience,20(6), 879.

    Google Scholar 

  • Cushman, F. (2015). From moral concern to moral constraint. Current Opinion in Behavioral Sciences,3, 58–62.

    Google Scholar 

  • Debove, S., Baumard, N., & André, J. B. (2016). Models of the evolution of fairness in the ultimatum game: A review and classification. Evolution Andhuman Behavior, 37(3), 245–254.

    Google Scholar 

  • De Sio, F. S. (2017). Killing by autonomous vehicles and the legal doctrine of necessity. Ethical Theory and Moral Practice,20(2), 411–429.

    Google Scholar 

  • Dennis, L., Fisher, M., Slavkovik, M., & Webster, M. (2016). Formal verification of ethical choices in autonomous systems. Robotics and Autonomous Systems,77, 1–14.

    Google Scholar 

  • Dietrich, F., & List, C. (2017). What matters and how it matters: a choice-theoretic representation of moral theories. Philosophical Review,126(4), 421–479.

    Google Scholar 

  • Doran, D., Schulz, S., & Besold, T. R. (2017). What does explainable AI really mean? A new conceptualization of perspectives. arXiv preprint arXiv:1710.00794.

  • Doris, J. M. (2002). Lack of character: Personality and moral behavior. Cambridge: Cambridge University Press.

    Google Scholar 

  • Dretske, F. (1994). If you can't make one, you don't know how it works. Midwest Studies in Philosophy, 19, 468–482.

    Google Scholar 

  • Driver, J. (2005). Normative ethics. In F. Jackson & M. Smith (Eds.), The Oxford Handbook of Contemporary Philosophy (pp. 31–62). Oxford: Oxford University Press.

    Google Scholar 

  • Elgin, C. Z. (2017). True enough. Cambridge: MIT Press.

    Google Scholar 

  • Everitt, T., Krakovna, V., Orseau, L., Hutter, M., & Legg, S. (2017). Reinforcement learning with a corrupted reward channel. arXiv preprint. arXiv:1705.08417.

  • Everitt, T., Lea, G., & Hutter, M. (2018). AGI safety literature review. arXiv preprint arXiv:1805.01109.

  • Farrell, J. (1987). Cheap talk, coordination, and entry. The Rand Journal of Economics,18(1), 34–39.

    MathSciNet  Google Scholar 

  • Fehr, E., & Schmidt, K. (2003). Theories of fairness and reciprocity–evidence and economic applications. In Advances in economics and econometrics, 8th World Congress, Econometric Society Monographs.

  • Feng, C., Luo, Y. J., & Krueger, F. (2015). Neural signatures of fairness-related normative decision making in the ultimatum game: A coordinate-based meta-analysis. Human Brain Mapping,36(2), 591–602.

    Google Scholar 

  • Flanagan, O., Sarkissian, H., & Wong, D. (2007). Naturalizing ethics. In W. Sinnott-Armstrong (Ed.), Moral psychology, Vol. 1. The evolution of morality: Adaptations and innateness  (pp. 1–25). Cambridge: MIT Press.

    Google Scholar 

  • Fleetwood, J. (2017). Public health, ethics, and autonomous vehicles. American Journal of Public Health,107(4), 532–537.

    Google Scholar 

  • Forsythe, R., Horowitz, J. L., Savin, N. E., & Sefton, M. (1994). Fairness in simple bargaining experiments. Games and Economic Behavior, 6(3), 347–369.

    MATH  Google Scholar 

  • Gábor, Z., Kalmár, Z., & Szepesvári, C. (1998, July). Multi-criteria reinforcement learning. In ICML (Vol. 98, pp. 197–205). Chicago.

  • Glimcher, P. W. (2011). Foundations of neuroeconomic analysis. Oxford: OUP USA.

    Google Scholar 

  • Gogoll, J., & Müller, J. F. (2017). Autonomous cars: in favor of a mandatory ethics setting. Science and Engineering Ethics,23(3), 681–700.

    Google Scholar 

  • Güth, W., Schmittberger, R., & Schwarze, B. (1982). An experimental analysis of ultimatum bargaining. Journal of Economic Behavior & Organization,3(4), 367–388.

    Google Scholar 

  • Hadfield-Menell, D., Milli, S., Abbeel, P., Russell, S. J., & Dragan, A. (2017). Inverse reward design. In Advances in neural information processing systems, (pp. 6765–6774).

  • Hartmann, S. (1996). The world as a process: Simulations in the natural and social sciences. in Hegselmann, Mueller, and Troitzsch 1996: 77–100.

  • Hass, J. (2019). Valuation mechanisms in moral cognition. Behavioral and Brain Sciences. https://doi.org/10.1017/S0140525X18002686.

  • Henrich, J., Ensminger, J., McElreath, R., Barr, A., Barrett, C., Bolyanatz, A., et al. (2010a). Markets, religion, community size, and the evolution of fairness and punishment. Science,327(5972), 1480–1484.

    Google Scholar 

  • Henrich, J., Heine, S. J., & Norenzayan, A. (2010b). The weirdest people in the world? Behavioral and Brain Sciences,33(2–3), 61–83.

    Google Scholar 

  • Henrich, J., Heine, S. J., & Norenzayan, A. (2010c). Most people are not WEIRD. Nature,466(7302), 29.

    Google Scholar 

  • Himmelreich, J. (2018). Never mind the trolley: The ethics of autonomous vehicles in mundane situations. Ethical Theory and Moral Practice,21(3), 669–684.

    Google Scholar 

  • Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need?. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1–16).

  • Honarvar, A. R., & Ghasem-Aghaee, N. (2009). Casuist BDI-agent: a new extended BDI architecture with the capability of ethical reasoning. In International conference on artificial intelligence and computational intelligence (pp. 86–95). Berlin, Heidelberg: Springer.

  • Hoppenbrouwers, S. S., Van der Stigchel, S., Slotboom, J., Dalmaijer, E. S., & Theeuwes, J. (2015). Disentangling attentional deficits in psychopathy using visual search: Failures in the use of contextual information. Personality and Individual Differences,86, 132–138.

    Google Scholar 

  • Howard, D., & Muntean, I. (2017). Artificial moral cognition: moral functionalism and autonomous moral agency. In Philosophy and computing (pp. 121–159). Cham: Springer.

  • Iyer, R., Li, Y., Li, H., Lewis, M., Sundar, R., & Sycara, K. (2018). Transparency and explanation in deep reinforcement learning neural networks. In Proceedings of the 2018 AAAI/ACM conference on AI, ethics, and society (pp. 144–150).

  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence,1(9), 389–399.

    Google Scholar 

  • Kahneman, D., Knetsch, J. L., & Thaler, R. (1986). Fairness as a constraint on profit seeking: Entitlements in the market. The American Economic Review, 728–741.

  • Kamm, F. M. (2008). Intricate ethics: Rights, responsibilities, and permissable harm. Oxford: Oxford University Press.

    Google Scholar 

  • Ku, H. H., & Hung, Y. C. (2019). Framing effects of per-person versus aggregate prices in group meals. Journal of Consumer Behaviour,18(1), 43–52.

    Google Scholar 

  • Larson, J., Mattu, S., Kirchner, L., & Angwin, J. (2016). How we analyzed the COMPAS recidivism algorithm. ProPublica,5, 9.

    Google Scholar 

  • Leike, J., Martic, M., Krakovna, V., Ortega, P.A., Everitt, T., Lefrancq, A., Orseau, L. & Legg, S. (2017). AI safety gridworlds. arXiv preprint arXiv:1711.09883.

  • Liu, C., Xu, X., & Hu, D. (2014). Multiobjective reinforcement learning: A comprehensive overview. IEEE Transactions on Systems, Man, and Cybernetics: Systems,45(3), 385–398.

    Google Scholar 

  • Lugo, L., & Cooperman, A. (2013) A Portrait of Jewish Americans: Findings from a Pew Research Center Survey of U.S. Jews. Available online at: https://www.pewforum.org/2013/10/01/jewish-american-beliefs-attitudes-culture-survey/

  • Malle, B. F. (2016). Integrating robot ethics and machine morality: The study and design of moral competence in robots. Ethics and Information Technology,18(4), 243–256.

    Google Scholar 

  • Mannor, S., & Shimkin, N. (2004). A geometric approach to multi-criterion reinforcement learning. Journal of Machine Learning Research, 5, 325–360.

    MathSciNet  MATH  Google Scholar 

  • Marchetti, A., Baglio, F., Massaro, D., Griffanti, L., Rossetto, F., Sangiuliano Intra, F., et al. (2019). Can psychological labels influence the decision-making process in an unfair condition? Behavioral and neural evidences using the ultimatum game task. Journal of Neuroscience, Psychology, and Economics,12(2), 105.

    Google Scholar 

  • May, J. (2018). Regard for reason in the moral mind. Oxford: Oxford University Press.

    Google Scholar 

  • May, J. (2019). Defending optimistic rationalism: A reply to commentators. Behavioral and Brain Sciences. https://doi.org/10.1017/S0140525X19000967.

    Article  Google Scholar 

  • Millar, J., Lin, P., Abney, K., & Bekey, G. A. (2017). Ethics settings for autonomous vehicles (pp. 20–34). Cambridge: MIT Press.

    Google Scholar 

  • Moor, J. H. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems,21(4), 18–21.

    Google Scholar 

  • Morgan, M. S. (1999). Learning from models. Ideas in Context,52, 347–388.

    Google Scholar 

  • Nowak, M. A., Page, K. M., & Sigmund, K. (2000). Fairness versus reason in the ultimatum game. Science,289(5485), 1773–1775.

    Google Scholar 

  • Nyholm, S., & Smids, J. (2016). The ethics of accident-algorithms for self-driving cars: An applied trolley problem? Ethical Theory and Moral Practice,19(5), 1275–1289.

    Google Scholar 

  • Omohundro, S. M. (2008). The basic AI drives. In AGI (Vol. 171, pp. 483–492).

  • Padoa-Schioppa, C. (2011). Neurobiology of economic choice: A good-based model. Annual Review of Neuroscience,34, 333–359.

    Google Scholar 

  • Picard, R. (1997). Affective computing. Cambridge: MIT Press.

    Google Scholar 

  • Rand, D. G., Tarnita, C. E., Ohtsuki, H., & Nowak, M. A. (2013). Evolution of fairness in the one-shot anonymous Ultimatum Game. Proceedings of the National Academy of Sciences,110(7), 2581–2586.

    MathSciNet  MATH  Google Scholar 

  • Roff, H. Expected utilitarianism, manuscript.

  • Rosen, J. B., Rott, E., Ebersbach, G., & Kalbe, E. (2015). Altered moral decision-making in patients with idiopathic Parkinson’s disease. Parkinsonism & Related Disorders,21(10), 1191–1199.

    Google Scholar 

  • Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. Ai Magazine,36(4), 105–114.

    Google Scholar 

  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Malaysia: Pearson Education Limited.

    MATH  Google Scholar 

  • Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the ultimatum game. Science,300(5626), 1755–1758.

    Google Scholar 

  • Scheutz, M., & Malle, B. F. (2017). Moral robots. The Routledge Handbook of Neuroethics, Nueva York, Routledge/Taylor & Francis.

  • Schroeder, T., Roskies, A. L., & Nichols, S. B. (2010). Moral motivation. In J. Doris (Ed.), The Moral Psychology Handbook. Oxford: Oxford University Press.

    Google Scholar 

  • Shenhav, A., & Greene, J. D. (2010). Moral judgments recruit domain-general valuation mechanisms to integrate representations of probability and magnitude. Neuron,67(4), 667–677.

    Google Scholar 

  • Shevlin, H. De-skilling and social necessity, manuscript.

  • Sinnott-Armstrong, W., Mallon, R., Mccoy, T., & Hull, J. G. (2008). Intention, temporal order, and moral judgments. Mind & Language,23(1), 90–106.

    Google Scholar 

  • Soares, N., Fallenstein, B., Armstrong, S., & Yudkowsky, E. (2015). Corrigibility. In Workshops at the twenty-ninth AAAI conference on artificial intelligence.

  • Sripada, C. S., & Stich, S. (2005). A framework for the psychology of norms. The Innate Mind,2, 280–301.

    Google Scholar 

  • Sripada, C. S., & Stich, S. (2006). A framework for the psychology of norms. The Innate Mind, 2, 280–301.

    Google Scholar 

  • Sterelny, K., & Fraser, B. (2017). Evolution and moral realism. The British Journal for the Philosophy of Science,68(4), 981–1006.

    Google Scholar 

  • Sutton, R. S. (2019). The bitter lesson. http://www.incompleteideas.net/IncIdeas/BitterLesson.html.

  • Sutton, R. S., & Barto, A. G. (1998). Introduction to reinforcement learning (vol. 135). Cambridge: MIT press.

  • Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.

  • Taylor, J., Yudkowsky, E., LaVictoire, P., & Critch, A. (2016). Alignment for advanced machine learning systems. Berkeley: Machine Intelligence Research Institute.

    Google Scholar 

  • Thaler, R. H. (1988). Anomalies: The ultimatum game. Journal of economic perspectives,2(4), 195–206.

    Google Scholar 

  • Tracer, D. (2004). Market integration, reciprocity and fairness in rural papua new guinea: Results from a twovillage ultimatum game study. Artefactual Field Experiments 00112. The Field Experiments Website. Available online at: https://ideas.repec.org/p/feb/artefa/00112.html.

  • Vallor, S. (2015). Moral deskilling and upskilling in a new machine age: Reflections on the ambiguous future of character. Philosophy & Technology,28(1), 107–124.

    Google Scholar 

  • Vamplew, P., Dazeley, R., Foale, C., Firmin, S., & Mummery, J. (2018). Human-aligned artificial intelligence is a multiobjective problem. Ethics and Information Technology,20(1), 27–40.

    Google Scholar 

  • Vanderelst, D., & Winfield, A. (2018). An architecture for ethical robots inspired by the simulation theory of cognition. Cognitive Systems Research,48, 56–66.

    Google Scholar 

  • Van Moffaert, K., Drugan, M. M., & Nowé, A. (2013). Hypervolume-based multi-objective reinforcement learning. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 352-366). Springer, Berlin, Heidelberg.

  • Van Moffaert, K., & Nowé, A. (2014). Multi-objective reinforcement learning using sets of pareto dominating policies. The Journal of Machine LearningResearch, 15(1), 3483–3512.

    MathSciNet  MATH  Google Scholar 

  • Wallach, W., & Allen, C. (2008). Moral machines: Teaching robots right from wrong. Oxford: Oxford University Press.

    Google Scholar 

  • Wallach, W., Franklin, S., & Allen, C. (2010). A conceptual and computational model of moral decision making in human and artificial agents. Topics in Cognitive Science,2(3), 454–485.

    Google Scholar 

  • Wallach, W., & Marchant, G. (2019). Toward the agile and comprehensive international governance of AI and Robotics. Proceedings of the IEEE,107(3), 505–508.

    Google Scholar 

  • Wei, C., Zheng, L., Che, L., Cheng, X., Li, L., & Guo, X. (2018). Social support modulates neural responses to unfairness in the ultimatum game. Frontiers in Psychology,9, 182.

    Google Scholar 

  • Winfield, A. (2019). An updated round up of ethical principles of robotics and AI [Blog post]. Retrieved from: http://alanwinfield.blogspot.com/2019/04/an-updated-round-up-of-ethical.html?m=1.

  • Winfield, A. F., Michael, K., Pitt, J., & Evers, V. (2019). Machine ethics: the design and governance of ethical AI and autonomous systems. Proceedings of the IEEE,107(3), 509–517.

    Google Scholar 

  • Wolf, S. (1982). Moral saints. The Journal of Philosophy, 79(8), 419–439.

    Google Scholar 

  • Woodward, James. (2003). Making things happen: A theory of causal explanation. Oxford: Oxford University Press. https://doi.org/10.1093/0195155270.001.0001.

    Book  Google Scholar 

  • Yang, R., Sun, X., & Narasimhan, K. (2019). A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation. In Advances in Neural Information Processing Systems (pp. 14610–14621).

  • Zhong, S., Israel, S., Shalev, I., Xue, H., Ebstein, R. P., et al. (2010). Dopamine D4 receptor gene associated with fairness preference in ultimatum game. PLoSONE, 5(11), e13765. https://doi.org/10.1371/journal.pone.0013765.

    Article  Google Scholar 

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    Julia Haas

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Haas, J. Moral Gridworlds: A Theoretical Proposal for Modeling Artificial Moral Cognition. Minds & Machines 30, 219–246 (2020). https://doi.org/10.1007/s11023-020-09524-9

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