Evolution of a complex anti-predator niche construction in a physical 2D predator-prey simulation and a feature analysis of defensive structures using a deep auto-encoder
Naoaki Chiba, Reiji Suzuki, Takaya Arita
niche construction, evolution, physically simulated environment, complexity, deep learning
Niche construction is a process in which organisms modify the selection pressures on themselves and others through their ecological activities. While effects of niche construction on evolution have been discussed using simple theoretical models, not much is known about the evolution of complex and physically-grounded niche construction such as beaver dams. Our purpose is to clarify what conditions and what kind of complex and various niche-constructing behaviors evolve in physically-grounded environments. We focus on a predator-prey relationship because it is one of the most fundamental ecological relationships among species and had brought about a wide variety of anti-predator adaption. We constructed an evolutionary model in which a prey has to prevent itself from being captured by a predator through construction of a structure composed of objects in a 2D physically simulated environment using the LiquidFun. Moreover, we used a deep learning technique for further analysis of emerged adaptive structures. We show that there was a large diversity in the emerged adaptive structure in the case that the number of available resources was intermediate. It also turned out that there was a positive relationship between the number of available resources and the average fitness. The detailed analysis shows that there were three typical types of adaptive structures. A “shell strategy” encloses the whole body of a prey with many objects, while “barnacles strategy” encloses it by moving toward left and using objects and field tiles. A“wall strategy” creates a tall wall between a prey and a predator.