The Biosphere is a bountiful source of inspiration for the biologically inclined scientist, though one may be seized by the twists and turns of its complexity. Artificial Life emerged from the conundrum of condensing this overwhelming intricacy into a tractable volume of data. To tackle the distant challenge of studying the long-term dynamics of artificial ecosystems, we focused in this work our efforts on plantplant interactions in a simplified 3D setting. Through an extension of K. Sims' directed graphs, we devised a polyvalent genotype for artificial plants development. These individuals compete and collaborate with one another in a shared plot of earth subjected to dynamically changing environmental conditions. We illustrate and analyze how the use of multi-objective fitnesses generated a panel of diverse morphologies and strategies. Furthermore, we identify two driving forces of the emerge of self-reproduction and investigate their effect on self-sustainability.

Understanding the neurological implementation of emotions is a major research subject from biology to computer sciences that, in the latter case, takes many shapes: from accurate detection of human emotions to the emulation of plausible responses to stimuli. There is, however, room for a more bottom-up approach in which we would thrive to recreate emotions from undifferentiated elementary building blocks.In this article, we used virtual creatures that interact with their environment through a low-level perception/cognition/action loop to demonstrate their potential for fear responses. Embedded in a physical environment in a typical prey/predator setting, they develop strategies for foraging while minimizing their exposure to danger.