In this thesis, we explore the use of complex systems to study learning and adaptation in natural and artificial systems. The goal is to develop autonomous systems that can learn without supervision, develop on their own, and become increasingly complex over time. Complex systems are identified as a suitable framework for understanding these phenomena due to their ability to exhibit growth of complexity. Being able to build learning algorithms that require limited to no supervision would enable greater flexibility and adaptability in various applications. By understanding the fundamental principles of learning in complex systems, we hope to advance our ability to design and implement practical learning algorithms in the future. This thesis makes the following key contributions: the development of a general complexity metric that we apply to search for complex systems that exhibit growth of complexity, the introduction of a coarse-graining method to study computations in large-scale complex systems, and the development of a metric for learning efficiency as well as a benchmark dataset for evaluating the speed of learning algorithms. Our findings add substantially to our understanding of learning and adaptation in natural and artificial systems. Moreover, our approach contributes to a promising new direction for research in this area. We hope these findings will inspire the development of more effective and efficient learning algorithms in the future.

翻译：本论文探讨如何利用复杂系统研究自然系统与人工系统中的学习与适应过程。目标是开发能够在无监督条件下自主学习、自主进化并随时间推移持续提升复杂性的自主系统。复杂系统因其展现复杂性增长的特性，被确定为理解这些现象的适切框架。构建仅需少量甚至无需监督的学习算法，将显著提升各类应用场景的灵活性与适应性。通过理解复杂系统中学习的基本原理，我们期望未来能进一步提升设计与实现实用学习算法的能力。本论文的主要贡献包括：提出一种通用复杂性度量方法，并将其应用于寻找具有复杂性增长特征的复杂系统；引入粗粒化方法以研究大规模复杂系统中的计算过程；开发学习效率评估指标，并建立用于评估学习算法速度的基准数据集。这些研究结果显著深化了我们对自然系统与人工系统中学习与适应机制的理解。此外，我们的方法为这一研究领域开辟了有价值的新方向。期待这些发现能推动未来更高效学习算法的开发。