A Computational Model for Simulation of Empathy and Moral Behavior

Emotions and feelings are now considered as decisive in the human intelligent decision process. In particular, social emotions would help us to enhance the group and cooperate. It is still a matter of debate the what that motivates biological creatures to cooperate or not with their group. Would all kinds of cooperation hide a selfish interest, or would it exist truly altruism? If we pore over those questions from a human perspective, we end up passing through moral behavior and three kinds of individuals: the moral, immoral and amoral. If we move from biological subjects onto artificial agents, it is a complex matter to go without ad hoc mechanisms to bring up cooperation in utility-based computational approaches. We decided to take inspiration from moral behavior as a way of moving toward cooperation in Multiagent Systems. Our leading hypothesis relies on the idea that cooperation can emerge from the assistance of emotions and moral behavior during the process of decision making even when selfish behavior is rewarded by high reinforcements. The analogy with moral behavior is promoted through simulating the feeling of empathy. The importance of the empathy feeling is its function on regulating the agents priorities, enabling the selection of actions that may not be the best selfish selection, since non selfish decision making may be crucial to equalize the interactions among agents and bring up cooperation. We depict herein our bioinspired computational multiagent architecture (so-called MultiA) composed by artificial emotions, feelings and by an Empathy Module responsible for providing an action selection mechanism that rudimentarilly mimic both moral and immoral behaviors. Sensorial information is triggered by the environment, then, the computational architecture transforms it into basic and social artificial emotions and feelings. Thereat its own emotions are employed to estimate the current state of other agents through an Empathy module. Finally, its artificial feelings provide a measure (termed well-being) of its performance in response to the environment. Through that measure and reinforcement learning techniques, the architecture learns a mapping from emotions to actions. While facing high rewards to selfish behavior, the MultiA agents that adopt the cooperative strategy do so from the result of an empathy feeling (high empathy levels) regulating the agents priorities, acting as a moral agent. The MultiA agents that do not adopt the cooperative strategy select selfish actions, and do so as a result of low empathy levels, acting as an immoral agent. The MultiA mechanism of