All cognitive agents are composite beings. Specifically, complex living agents consist of cells, which are themselves competent sub-agents navigating physiological and metabolic spaces. Behavior science, evolutionary developmental biology, and the field of machine intelligence all seek an answer to the scaling of biological cognition: what evolutionary dynamics enable individual cells to integrate their activities to result in the emergence of a novel, higher-level intelligence that has goals and competencies that belong to it and not to its parts? Here, we report the results of simulations based on the TAME framework, which proposes that evolution pivoted the collective intelligence of cells during morphogenesis of the body into traditional behavioral intelligence by scaling up the goal states at the center of homeostatic processes. We tested the hypothesis that a minimal evolutionary framework is sufficient for small, low-level setpoints of metabolic homeostasis in cells to scale up into collectives (tissues) which solve a problem in morphospace: the organization of a body-wide positional information axis (the classic French Flag problem). We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve its target morphology as well as the ability to recover from perturbation (robustness) and long-term stability (even though neither of these was directly selected for). Moreover we observed unexpected behavior of sudden remodeling long after the system stabilizes. We tested this prediction in a biological system - regenerating planaria - and observed a very similar phenomenon. We propose that this system is a first step toward a quantitative understanding of how evolution scales minimal goal-directed behavior (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.
翻译:具体地说, 复杂的活物剂是由细胞组成的细胞组成。 行为科学、 进化发展生物学和机器智能领域都寻求生物认知规模扩大的答案: 哪些进化动态使单个细胞能够整合它们的活动,从而形成具有属于它而不是其部分的目标和能力的新颖的、 更高层次的智能? 在这里, 我们报告基于TAME框架的模拟结果, 该框架建议进化将细胞在生理和代谢空间的摩擦形成过程中的集体智能凝聚到传统的行为智能中。 行为科学、 进化发展生物学和机器智能领域都寻求生物认知规模扩大的答案: 一个最小进化框架足以让细胞中小的、 低层次的代谢性原生常态集成到集体空间( 问题) : 组织一个机构范围的定位信息轴( 典型的法国国旗问题 ) 。 我们发现, 这些突变变变的动力动力系统通过扩大目标状态, 展示出一系列预测的进化能力, 包括不断恢复的动态压力( 我们观察的进化的系统, ) 直变变变变变的系统, 直向, 直向的进的进变变变的系统, 向的进的系统, 直向的进化的动力, 度( 我们的进进进变向的动力的动力的动力的动力压的动力的动力的动力的动力的动力的动力压, 直向的动力压压, 的动力压, 的动力压, 的动力压, 压力, 直压, 直压的动力压的动力压压, 压力压压压压压压, 压力, 我们的系统,我们的系统, 我们的系统, 我们的系统, 直向的变向的变向的变向的变向的变向的变向的变向的变向的变压的变向的变向, 向, 向, 向, 向, 直向,我们的变向, 我们的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向的变向,