Can coupled agent homeostasis create prosocial AI?
Empathic Coupling of Homeostatic States for Intrinsic Prosociality
This paper explores how artificial agents can learn prosocial behavior, like sharing resources, using a concept called "homeostatic reinforcement learning." It tests different types of empathy in simulated environments, finding that directly coupling the agents' internal states (affective empathy) is crucial for them to develop prosociality. Simply observing another agent's needs (cognitive empathy) isn't enough.
For LLM-based multi-agent systems, this suggests that internal representations shared or directly linked between agents might be necessary for collaborative behaviors to emerge. It highlights the importance of going beyond simply allowing LLMs to perceive each other's stated needs and exploring mechanisms that create a shared sense of "well-being" or motivation across agents.