Empowerment -- a domain independent, information-theoretic metric -- has previously been shown to assist in the evolutionary search for neural cellular automata (NCA) capable of homeostasis when employed as a fitness function. In our previous study, we successfully extended empowerment, defined as maximum time-lagged mutual information between agents' actions and future sensations, to a distributed sensorimotor system embodied as an NCA. However, the time-delay between actions and their corresponding sensations was arbitrarily chosen. Here, we expand upon previous work by exploring how the time scale at which empowerment operates impacts its efficacy as an auxiliary objective to accelerate the discovery of homeostatic NCAs. We show that shorter time delays result in marked improvements over empowerment with longer delays, when compared to evolutionary selection only for homeostasis. Moreover, we evaluate stability and adaptability of evolved NCAs, both hallmarks of living systems that are of interest to replicate in artificial ones. We find that short-term empowered NCA are more stable and are capable of generalizing better to unseen homeostatic challenges. Taken together, these findings motivate the use of empowerment during the evolution of other artifacts, and suggest how it should be incorporated to accelerate evolution of desired behaviors for them. Source code for the experiments in this paper can be found at: https://github.com/caitlingrasso/empowered-nca-II.

翻译：赋能——一种领域无关的信息论度量——先前已被证明在作为适应度函数时，能够辅助进化搜索具备稳态能力的神经元胞自动机（NCA）。在我们先前的研究中，我们成功地将赋能（定义为智能体行动与未来感知之间的最大时滞互信息）扩展到了一个以NCA为具身形式的分布式感觉运动系统。然而，行动与其对应感知之间的时间延迟是任意选择的。本文在先前工作的基础上，探讨了赋能运作的时间尺度如何影响其作为辅助目标在加速发现稳态NCA方面的效力。我们发现，与仅针对稳态进行进化选择相比，采用较短时间延迟的赋能相较于采用较长时间延迟的赋能能带来显著改进。此外，我们评估了进化所得NCA的稳定性和适应性，这两者是生命系统的标志性特征，也是人工系统复制中备受关注的特性。我们发现，短期赋能的NCA更稳定，并且能够更好地泛化到未见过的稳态挑战。综上所述，这些发现为在其他人工制品的进化过程中使用赋能提供了依据，并指明了应如何将其纳入以加速其期望行为的演化。本文实验的源代码可在以下网址找到：https://github.com/caitlingrasso/empowered-nca-II。