Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
In the simplest scenario of two co-evolving populations in competition with each other, fitness progress is achieved at disadvantage of the other population's fitness. The everchanging fitness landscape caused by the competing species (named the "Red Queen effect") makes the system dynamics more complex, but it also provides a set of advantages with respect to single-population evolution. Here we present results from an experiment with two mobile robots, a predator equipped with vision and a much faster prey with simpler sensors. Without any effort in fitness design, a set of interesting behaviors emerged in relatively short time, such as obstacle avoidance, straight navigation, visual tracking, object discrimination (robot vs. wall), object following, and others. Although such experiments cannot yet be performed in real-time on populations of robots for technical reasons, the approach seems promising.