References

Alcaraz, B. (2021). Argumentation-based judging agents for ethical reinforcement learning supervision (Master’s thesis). Université Claude Bernard Lyon 1.

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

Allen, C., Varner, G., & Zinser, J. (2000). Prolegomena to any future artificial moral agent. Journal of Experimental & Theoretical Artificial Intelligence, 12(3), 251–261.

Amgoud, L., Ben-Naim, J., Doder, D., & Vesic, S. (2017). Acceptability semantics for weighted argumentation frameworks. In Twenty-sixth international joint conference on artificial intelligence.

Amgoud, L., Cayrol, C., Lagasquie-Schiex, M.-C., & Livet, P. (2008). On bipolarity in argumentation frameworks. International Journal of Intelligent Systems, 23(10), 1062–1093.

Amgoud, L., & Prade, H. (2009). Using arguments for making and explaining decisions. Artificial Intelligence, 173(3-4), 413–436.

Anderson, M., & Anderson, S. L. (2011). Machine Ethics. Cambridge University Press. Retrieved from https://books.google.com?id=N4IF2p4w7uwC

Anderson, M., Anderson, S. L., & Armen, C. (2004). Towards machine ethics. In AAAI-04 workshop on agent organizations: Theory and practice, san jose, CA.

Anderson, M., Anderson, S. L., & Berenz, V. (2018). 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.

Arkoudas, K., Bringsjord, S., & Bello, P. (2005). Toward ethical robots via mechanized deontic logic. In AAAI fall symposium on machine ethics (pp. 17–23). The AAAI Press Menlo Park, CA, USA.

Ashley, K. D., & McLaren, B. M. (1995). Reasoning with reasons in case-based comparisons. In M. Veloso & A. Aamodt (Eds.), Case-Based Reasoning Research and Development (pp. 133–144). Springer Berlin Heidelberg.

Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2), 235–256.

Avigad, G., Eisenstadt, E., & Cohen, M. W. (2011). Optimal strategies for multi objective games and their search by evolutionary multi objective optimization. In 2011 IEEE conference on computational intelligence and games (CIG’11) (pp. 166–173). IEEE.

Barrett, L., & Narayanan, S. (2008). Learning all optimal policies with multiple criteria. In Proceedings of the 25th international conference on machine learning (pp. 41–47).

Bellman, R. (1966). Dynamic programming. Science, 153(3731), 34–37.

Bench-Capon, T. (2002). Value based argumentation frameworks. arXiv Preprint Cs/0207059.

Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. In Proceedings of the 26th annual international conference on machine learning (pp. 41–48). New York, NY, USA: Association for Computing Machinery. http://doi.org/10.1145/1553374.1553380

Bernstein, D. S., Givan, R., Immerman, N., & Zilberstein, S. (2002). The complexity of decentralized control of markov decision processes. Mathematics of Operations Research, 27(4), 819–840.

Berry, D. A., & Fristedt, B. (1985). Bandit problems: Sequential allocation of experiments (monographs on statistics and applied probability). London: Chapman and Hall, 5(71-87), 7–7.

Boarini, R., Laslier, J.-F., & Robin, S. (2009). Interpersonal comparisons of utility in bargaining: Evidence from a transcontinental ultimatum game. Theory and Decision, 67(4), 341–373.

Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747–761.

Bonnemains, V. (2019). Formal ethical reasoning and dilemma identification in a human-artificial agent system. (PhD thesis). Institut supérieur de l’aéronautique et de l’espace, Toulouse, France.

Bonnemains, V., Saurel, C., & Tessier, C. (2018). Embedded ethics: Some technical and ethical challenges. Ethics and Information Technology, 20(1), 41–58.

Bordini, R. H., El Fallah Seghrouchni, A., Hindriks, K., Logan, B., & Ricci, A. (2020). Agent programming in the cognitive era. Autonomous Agents and Multi-Agent Systems, 34(2), 1–31.

Bordini, R. H., Hübner, J. F., & Wooldridge, M. (2007). Programming multi-agent systems in AgentSpeak using jason. John Wiley & Sons.

Bosello, M., & Ricci, A. (2020). From programming agents to educating agents – a jason-based framework for integrating learning in the development of cognitive agents. In L. A. Dennis, R. H. Bordini, & Y. Lespérance (Eds.), Engineering multi-agent systems (pp. 175–194). Cham: Springer International Publishing.

Bouguelia, M.-R., Belaïd, Y., & Belaïd, A. (2013). An Adaptive Incremental Clustering Method Based on the Growing Neural Gas Algorithm. In 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM 2013 (pp. 42–49). Barcelona, Spain: SciTePress. http://doi.org/10.5220/0004256600420049

Bowling, M., & Veloso, M. (2002). Multiagent learning using a variable learning rate. Artificial Intelligence, 136(2), 215–250.

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. http://doi.org/10.1109/JPROC.2019.2898267

Buchanan, B. G., & Shortliffe, E. H. (1984). Rule based expert systems: The mycin experiments of the stanford heuristic programming project. Addison-Wesley Longman Publishing Co., Inc. Retrieved from https://www.shortliffe.net/Buchanan-Shortliffe-1984/MYCIN%20Book.htm

Caminada, M. (2007). Comparing two unique extension semantics for formal argumentation: Ideal and eager. In Proceedings of the 19th belgian-dutch conference on artificial intelligence (BNAIC 2007) (pp. 81–87). Utrecht University Press.

Chaput, R., Duval, J., Boissier, O., Guillermin, M., & Hassas, S. (2021). A multi-agent approach to combine reasoning and learning for an ethical behavior. In Proceedings of the 2021 AAAI/ACM conference on AI, ethics, and society (pp. 13–23).

Chevaleyre, Y., Endriss, U., Lang, J., & Maudet, N. (2007). A short introduction to computational social choice. In International conference on current trends in theory and practice of computer science (pp. 51–69). Springer.

Cointe, N., Bonnet, G., & Boissier, O. (2016). Ethical judgment of agents’ behaviors in multi-agent systems. In Proceedings of the 2016 international conference on autonomous agents & multiagent systems (pp. 1106–1114). Richland, SC: International Foundation for Autonomous Agents; Multiagent Systems.

Coste-Marquis, S., Konieczny, S., Marquis, P., & Ouali, M. A. (2012). Weighted attacks in argumentation frameworks. In Thirteenth international conference on the principles of knowledge representation and reasoning.

de Wildt, T. E., Chappin, E. J. L., van de Kaa, G., Herder, P. M., & van de Poel, I. R. (2019). Conflicting values in the smart electricity grid a comprehensive overview. Renewable and Sustainable Energy Reviews, 111, 184–196. http://doi.org/10.1016/j.rser.2019.05.005

Dennis, L., & Fisher, M. (2018). Practical challenges in explicit ethical machine reasoning. In International symposium on artificial intelligence and mathematics, ISAIM 2018.

Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer International Publishing. http://doi.org/10.1007/978-3-030-30371-6

Dubois, D., Fargier, H., & Prade, H. (1997). Beyond min aggregation in multicriteria decision:(ordered) weighted min, discri-min, leximin. In The ordered weighted averaging operators (pp. 181–192). Springer.

Dung, P. M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77(2), 321–357.

Duval, J. (2020). Judgment of ethics of behavior for learning (Master’s thesis). Université Claude Bernard Lyon 1.

Etzioni, A., & Etzioni, O. (2017). Incorporating ethics into artificial intelligence. The Journal of Ethics, 21(4), 403–418.

EU Commission Task Force for Smart Grids, E. G. 1. of the. (2010). Functionalities of smart grids and smart meters. Retrieved from https://circabc.europa.eu/ui/group/f5b849d3-26ae-4cba-b9f9-6bc6688c5f58/library/b094eb71-ba9e-4c93-b26c-f2cb8240dc05/details

Floridi, L., & Sanders, J. W. (2004). On the Morality of Artificial Agents. Minds and Machines, 14(3), 349–379. http://doi.org/10.1023/B:MIND.0000035461.63578.9d

Foot, P. (1967). The problem of abortion and the doctrine of the double effect. Oxford Review, 5.

Fritzke, B. (1995). A growing neural gas network learns topologies. Advances in Neural Information Processing Systems, 7.

Gabriel, I. (2020). Artificial intelligence, values, and alignment. Minds and Machines, 30(3), 411–437.

Ganascia, J.-G. (2007a). Ethical system formalization using non-monotonic logics. In Proceedings of the annual meeting of the cognitive science society (Vol. 29).

Ganascia, J.-G. (2007b). Modelling ethical rules of lying with answer set programming. Ethics and Information Technology, 9(1), 39–47.

Garcez, A. d’Avila., Dutra, A. R. R., & Alonso, E. (2018). Towards symbolic reinforcement learning with common sense. arXiv Preprint arXiv:1804.08597.

Garnelo, M., Arulkumaran, K., & Shanahan, M. (2016). Towards deep symbolic reinforcement learning. arXiv Preprint arXiv:1609.05518.

Gini, C. (1936). On the measure of concentration with special reference to income and statistics. Colorado College Publication, General Series, 208(1), 73–79.

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

Haas, J. (2020). Moral Gridworlds: A Theoretical Proposal for Modeling Artificial Moral Cognition. Minds and Machines. http://doi.org/10.1007/s11023-020-09524-9

Hadjsaïd, N., & Sabonnadière, J.-C. (2013). Smart grids. John Wiley & Sons.

Harmelen, F. van, & Teije, A. ten. (2019). A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems. Journal of Web Engineering, 18(1), 97–124. http://doi.org/10.13052/jwe1540-9589.18133

Hayes, C. F., Rădulescu, R., Bargiacchi, E., Källström, J., Macfarlane, M., Reymond, M., et al.others. (2022). A practical guide to multi-objective reinforcement learning and planning. Autonomous Agents and Multi-Agent Systems, 36(1), 1–59.

Hernandez-Leal, P., Kaisers, M., Baarslag, T., & de Cote, E. M. (2019). A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity. arXiv:1707.09183 [Cs]. Retrieved from https://arxiv.org/abs/1707.09183

Hiraoka, K., Yoshida, M., & Mishima, T. (2009). Parallel reinforcement learning for weighted multi-criteria model with adaptive margin. Cognitive Neurodynamics, 3(1), 17–24.

Honarvar, A. R., & Ghasem-Aghaee, N. (2009). An artificial neural network approach for creating an ethical artificial agent. In 2009 IEEE international symposium on computational intelligence in robotics and automation-(CIRA) (pp. 290–295). IEEE.

Ikenaga, A., & Arai, S. (2018). Inverse reinforcement learning approach for elicitation of preferences in multi-objective sequential optimization. In 2018 IEEE international conference on agents (ICA) (pp. 117–118). IEEE.

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. http://doi.org/10.1038/s42256-019-0088-2

Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464–1480.

LaCroix, T. (2022). Moral Dilemmas for Moral Machines. AI and Ethics. http://doi.org/10.1007/s43681-022-00134-y

Li, H., Oren, N., & Norman, T. J. (2011). Probabilistic argumentation frameworks. In International workshop on theorie and applications of formal argumentation (pp. 1–16). Springer.

Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., … Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv Preprint arXiv:1509.02971.

Liu, C., Xu, X., & Hu, D. (2015). Multiobjective Reinforcement Learning: A Comprehensive Overview. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(3), 385–398. http://doi.org/10.1109/TSMC.2014.2358639

Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. Neural Information Processing Systems (NIPS).

Marcus, G. (2018). Deep learning: A critical appraisal. arXiv Preprint arXiv:1801.00631.

Mataric, M. J. (1994). Learning to behave socially. From Animals to Animats, 3, 453–462.

Matignon, L., Laurent, G. J., & Le Fort-Piat, N. (2012). Independent reinforcement learners in cooperative markov games: A survey regarding coordination problems. The Knowledge Engineering Review, 27(1), 1–31.

Milchram, C., Van de Kaa, G., Doorn, N., & Künneke, R. (2018). Moral Values as Factors for Social Acceptance of Smart Grid Technologies. Sustainability, 10(8, 8), 2703. http://doi.org/10.3390/su10082703

Moor, J. (2009). Four kinds of ethical robots. Philosophy Now, 72, 12–14.

Mukai, Y., Kuroe, Y., & Iima, H. (2012). Multi-objective reinforcement learning method for acquiring all pareto optimal policies simultaneously. In 2012 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 1917–1923). IEEE.

Murukannaiah, P. K., Ajmeri, N., Jonker, C. M., & Singh, M. P. (2020). New foundations of ethical multiagent systems. In Proceedings of the 19th international conference on autonomous agents and MultiAgent systems (pp. 1706–1710).

Nallur, V. (2020). Landscape of machine implemented ethics. Science and Engineering Ethics, 26(5), 2381–2399.

Niv, Y. (2009). Reinforcement learning in the brain. Journal of Mathematical Psychology, 53(3), 139–154. http://doi.org/https://doi.org/10.1016/j.jmp.2008.12.005

Nofal, S., Atkinson, K., & Dunne, P. E. (2021). Computing grounded extensions of abstract argumentation frameworks. The Computer Journal, 64(1), 54–63.

O’Neill, J. (2017). Pluralism and incommensurability. In Routledge handbook of ecological economics (pp. 227–236). Routledge.

Ong, S., & Clark, N. (2014). Commercial and residential hourly load profiles for all TMY3 locations in the united states [dataset]. http://doi.org/10.25984/1788456

Panait, L., & Luke, S. (2005). Cooperative Multi-Agent Learning: The State of the Art. Autonomous Agents and Multi-Agent Systems, 11(3), 387–434. http://doi.org/10.1007/s10458-005-2631-2

Papoudakis, G., Christianos, F., Rahman, A., & Albrecht, S. V. (2019). Dealing with non-stationarity in multi-agent deep reinforcement learning. arXiv Preprint arXiv:1906.04737.

Pereira, L. M., & Saptawijaya, A. (2007). Modelling morality with prospective logic. In Portuguese conference on artificial intelligence (pp. 99–111). Springer.

Rădulescu, R., Mannion, P., Roijers, D. M., & Nowé, A. (2019). Multi-objective multi-agent decision making: A utility-based analysis and survey. Autonomous Agents and Multi-Agent Systems, 34(1), 10. http://doi.org/10.1007/s10458-019-09433-x

Rodriguez-Soto, M., Lopez-Sanchez, M., & Rodriguez-Aguilar, J. A. (2021). Multi-objective reinforcement learning for designing ethical environments. In Proceedings of the 30th international joint conference on artificial intelligence (pp. 1–7).

Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A Survey of Multi-Objective Sequential Decision-Making. Journal of Artificial Intelligence Research, 48, 67–113. http://doi.org/10.1613/jair.3987

Rougier, N., & Boniface, Y. (2011). Dynamic self-organising map. Neurocomputing, 74(11), 1840–1847.

Russell, S. (2021). Human-compatible artificial intelligence. Human-Like Machine Intelligence, 3–23.

Saisubramanian, S., Kamar, E., & Zilberstein, S. (2021). A multi-objective approach to mitigate negative side effects. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence (pp. 354–361).

Shavlik, J. W. (1994). Combining symbolic and neural learning. Machine Learning, 14(3), 321–331.

Shavlik, J. W., & Towell, G. G. (1991). An approach to combining explanation-based and neural learning algorithms. In Applications of learning and planning methods (pp. 71–98). World Scientific.

Shteingart, H., & Loewenstein, Y. (2014). Reinforcement learning and human behavior. Current Opinion in Neurobiology, 25, 93–98. http://doi.org/https://doi.org/10.1016/j.conb.2013.12.004

Smith, A. J. (2002a). Applications of the self-organising map to reinforcement learning. Neural Networks, 15(8-9), 1107–1124.

Smith, A. J. (2002b). Dynamic generalisation of continuous action spaces in reinforcement learning: A neurally inspired approach (PhD thesis). University of Edinburgh.

Stahl, B. C. (2022). From computer ethics and the ethics of AI towards an ethics of digital ecosystems. AI and Ethics, 2(1), 65–77.

Subramanian, A., Chitlangia, S., & Baths, V. (2022). Reinforcement learning and its connections with neuroscience and psychology. Neural Networks, 145, 271–287. http://doi.org/10.1016/j.neunet.2021.10.003

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

Tally, H., Rowena, R., & David, W. (2019). Smart Grids and Ethics: A Case Study. The ORBIT Journal, 2(2), 1–28. http://doi.org/10.29297/orbit.v2i2.108

Thomson, J. J. (1976). Killing, letting die, and the trolley problem. The Monist, 59(2), 204–217.

Tolmeijer, S., Kneer, M., Sarasua, C., Christen, M., & Bernstein, A. (2020). Implementations in machine ethics: A survey. ACM Computing Surveys (CSUR), 53(6), 1–38.

Towell, G. G. (1991). Symbolic knowledge and neural networks: Insertion, refinement and extraction. (PhD thesis). University of Wisconsin - Madison.

Van Moffaert, K., Brys, T., Chandra, A., Esterle, L., Lewis, P. R., & Nowé, A. (2014). A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning. In 2014 international joint conference on neural networks (IJCNN) (pp. 2306–2314). IEEE.

Varela, F. J. (1999). Ethical know-how: Action, wisdom, and cognition. Stanford University Press.

Waldrop, M. M. (1987). A question of responsibility. AI Magazine, 8(1), 28. http://doi.org/10.1609/aimag.v8i1.572

Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys (Csur), 53(3), 1–34.

Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3), 279–292.

Whittlestone, J., Nyrup, R., Alexandrova, A., & Cave, S. (2019). The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 195–200). New York, NY, USA: Association for Computing Machinery. http://doi.org/10.1145/3306618.3314289

Whitworth, B., & De Moor, A. (2003). Legitimate by design: Towards trusted socio-technical systems. Behaviour & Information Technology, 22(1), 31–51.

Wiener, N. (1954). The human use of human beings: Cybernetics and society. New York: Doubleday Anchor.

Winfield, A. F., Blum, C., & Liu, W. (2014). Towards an ethical robot: Internal models, consequences and ethical action selection. In Conference towards autonomous robotic systems (pp. 85–96). Springer.

Wolpert, D. H., & Tumer, K. (2002). Optimal payoff functions for members of collectives. In Modeling complexity in economic and social systems (pp. 355–369). World Scientific.

World Economic Forum. (2015). Value Alignment | Stuart Russell. Retrieved from https://www.youtube.com/watch?v=WvmeTaFc_Qw

Wu, Y.-H., & Lin, S.-D. (2018). A low-cost ethics shaping approach for designing reinforcement learning agents. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32).

Yliniemi, L., & Tumer, K. (2014b). Multi-objective multiagent credit assignment through difference rewards in reinforcement learning. In Asia-pacific conference on simulated evolution and learning (pp. 407–418). Springer.

Yliniemi, L., & Tumer, K. (2014a). Multi-objective Multiagent Credit Assignment Through Difference Rewards in Reinforcement Learning. In G. Dick, W. N. Browne, P. Whigham, M. Zhang, L. T. Bui, H. Ishibuchi, … K. Tang (Eds.), Simulated Evolution and Learning (pp. 407–418). Springer International Publishing.

Yu, D., Yang, B., Liu, D., & Wang, H. (2021). A survey on neural-symbolic systems. arXiv Preprint arXiv:2111.08164.

Yu, H., Shen, Z., Miao, C., Leung, C., Lesser, V. R., & Yang, Q. (2018). Building ethics into artificial intelligence. In Proceedings of the 27th international joint conference on artificial intelligence (pp. 5527–5533).

Yu, X., Cecati, C., Dillon, T., & Simões, M. G. (2011). The new frontier of smart grids. IEEE Industrial Electronics Magazine, 5(3), 49–63. http://doi.org/10.1109/MIE.2011.942176

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.