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Autonomous Agents and Ethical Decision-Making

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Abstract

Machine ethics, also known as artificial morality, is a newly emerging field concerned with ensuring appropriate behavior of machines toward humans and other machines. In this article, we discuss the importance of machine ethics and present a computational model of ethical decision-making for autonomous agents. The proposed model implements a mechanism for integrating the results of diverse assessments into a unique cue, and takes into account the agent’s preferences, good and bad past experiences, ethical rules, and current emotional state as the main factors involved in choosing the most appropriate option. The design of the model is based on theories and models developed in fields such as neuroscience, psychology, artificial intelligence, and cognitive informatics. In particular, the model attempts to emulate neural mechanisms of the human brain involved in ethical decision-making.

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Correspondence to José-Antonio Cervantes.

Appendices

Appendix 1: Technical Information About First Case Study

The fuzzy rules used in the primary evaluation of option one “stop the car” in order to give way to a pedestrian are:

figure a

The fuzzy rules used in the primary evaluation of option two “continue driving” are:

figure b

The experiences given to the agent related to option one “stop the car” in order to give way to a pedestrian are:

figure c

The experiences given to the agent related to option two “continue driving” are:

figure d

Appendix 2: Fuzzy Ethical Rules Used in Both Cases

The fuzzy rules used by the agent in both cases are:

figure e

The norms and rules of the agent:

figure f

Appendix 3: Technical Information About Second Case

The fuzzy rules used in the primary evaluation of both options are:

figure g

The experiences of the agent in the second case are the following:

figure h

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Cervantes, JA., Rodríguez, LF., López, S. et al. Autonomous Agents and Ethical Decision-Making. Cogn Comput 8, 278–296 (2016). https://doi.org/10.1007/s12559-015-9362-8

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