Homeostatic Design Confers

Homeostatic design in artificial intelligence focuses on creating systems that maintain internal balance, mirroring biological homeostasis, to enhance robustness, adaptability, and safety. Current research explores this through various model architectures, including modular reinforcement learning agents with competing drives and spiking neural networks incorporating homeostatic plasticity, often leveraging continuous time and space frameworks. This approach offers significant potential for improving AI safety and efficiency by enabling agents to better handle conflicting objectives, adapt to changing environments, and learn more effectively from experience, with applications ranging from self-driving cars to complex robotic systems.