Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots

Abstract

One of the key innovations during the evolution of life on earth has been the emergence of efficient communication systems, yet little is known about the causes and consequences of the great diversity within and between species. By conducting experimental evolution in 20 independently evolving populations of cooperatively foraging simulated robots, we found that historical contingency in the occurrence order of novel phenotypic traits resulted in the emergence of two distinct communication strategies. The more complex foraging strategy was less efficient than the simpler strategy. However, when the 20 populations were placed in competition with each other, the populations with the more complex strategy outperformed the populations with the less complex strategy. These results demonstrate a tradeoff between communication efficiency and robustness and suggest that stochastic events have important effects on signal evolution and the outcome of competition between distinct populations.

Social complexity as a proximate and ultimate factor in communicative complexity

“…An intriguing example of the potential for neutral evolution to produce communicative complexity is presented by Wischmann et al [146] who studied the trajectory of signal evolution in replicated populations of cooperatively foraging robots. Robots could signal to one another by flashing blue and green lights.…”

Evolution of music by public choice

“…For this reason, inspired by studies of experimental evolution in microbes (9,10), digital organisms (11)(12)(13)(14)(15)(16), and previous work on evolutionary music and art (17)(18)(19), we developed an artificial system for studying musical evolution called "DarwinTunes." Evolutionary music studies, to date, have either attempted to automate the selection process (20) or have focused on the development of single-user composition (21) and performance (22) aids.…”

Evolutionary Robotics: Model or Design?

“…In this context, the modeling effort is focused on the evolutionary dynamics. Wischmann et al (2012) showed that evolutionary drift can determine the communication strategy adopted by a population of robots engaged in a foraging scenario, and showed the existence of a trade-off between communication efficiency and robustness from invasion of different strategies. Mitri et al (2011) studied a population of evolving robots competing for food, and demonstrated that genetic relatedness influences the reliability of the emitted signal: the higher the genetic relatedness, the lower the signal ambiguity.…”

“…However, the continuous development of better robots with more precise and informative sensors, together with more advanced techniques and control approaches, has eroded the advantage that ER first manifested, at least in the single-robot domain (Thrun et al, 2005;Siegwart et al, 2011;Zucker et al, 2011). At the same time, the scope of ER studies significantly broadened, in the attempt to provide solutions to the control problems of ever more complex robotic systems Baldassarre et al, 2007), as well as to address problems relevant to cognitive sciences and evolutionary biology (see, for instance, Tuci et al, 2011;Wischmann et al, 2012). Unfortunately, this broadening has not led to the establishment of ER as a mature discipline.…”