The Evolution of Learning: Balancing adaptivity and stability in artificial agents
Kai Olav Ellefsen · 2014
A longstanding challenge in artificial intelligence is to create agents that learn, enabling them to interact with and adapt to a complex and changing world. A better understanding of the evolution of learning may help produce robust and adaptive agents, as well as shed light on open questions about the evolution of learning from biology. Evolutionary com-putation offers the benefits of precise experimental control, repeatability of experiments and rapid generational turnover – enabling experiments to test hypotheses that would be impossible or extremely time demanding to test in natural studies. The evolution of learning is influenced by the balance between the benefits offered by adaptivity and the costs (disadvantages) individuals pay for learning abilities. Such costs include forgetting previous knowledge, dangers of exploration and maintenance of neural structures for learning. This thesis focuses on how evolution regulates learning capacities to reap the benefits of being adaptive, while minimizing the costs of learning. The regu-lation of learning capacities is studied along three main axes: regulation through individ-ual lifetimes, regulation within a population facing varying environments and regulation