A Predator-Prey Scenario in a Virtual Ecosystem

One of the main topics in artificial life is the design of systems that exhibit some characteristics of living organisms. Among the great variety of biological systems that inspire and guide these researches and according to Bedau et al. (2003), three broad areas can be identified depending on their basic elements: (a) At the microscopic scale, chemical, cellular and tissular systems; Wet ALife synthesizes living systems out of biochemical substances, (b) At the mesoscopic scale, organismal and architecture systems; or the Soft ALife that uses simulations or other purely digital constructions that exhibit lifelike behavior, (c) At the macroscopic scale, collective and societal systems. In our model we try to blend at least (b) and (c) in the same simulation. In this abstract, we propose architecture to simulate a virtual ecosystem and present extended results. This ecosystem is populated with 3D artificial creatures that have to use a simple predator-prey scenario. Artificial behaviors are developed in order to control artificial creatures. The artificial creatures living in the ecosystem are divided into four classes: producers (plants), 2 kinds of consumers (herbivores and carnivores) and decomposers such as bacteria (Ouannes et al, 2014). First, we studied the behavior of herbivorous creatures (Ouannes et al, 2012), which feed on available resources in their environment. In this part of our ecosystem, we present a controller model, which controls physically the simulated creatures in a biologically inspired manner; by evolving the neural connection weights of a neural network to obtain some emerged strategies of a predator prey, in addition to foraging behaviors (Ouannes et al, 2012). Here, the resulting behaviors are obtained from a predator prey encounter in a shared environment, the physical parameters and those of evolution are the same used in our previous work (Ouannes et al, 2012), but with two populations coevolved using external fitness functions that have opposite goals. The resulting controllers have been evolved in accordance to the physical laws and generate motion, taking into account the Newtonian dynamics (see figure 1). [GRAPHICS] .