Machine Self-Evolution

摘要

For hundreds of years mankind has been fascinated with machines that display life-like appearance and behaviour.The early robots of the 19 th century were anthropomorphic mechanical devices composed of gears and springs that would precisely repeat a predetermined sequence of movements.Although a dramatic improvement in robotics took place during the 20 th century with the development of electronics, computer technology, and artificial sensors, most of today robots used in factory floors are not significantly different from ancient automatic devices because they are still programmed to precisely execute a pre-defined series of actions.Are these machines intelligent?In our opinion they are not; they simply reflect the intelligence of the engineers that designed and programmed them.In the early 90s, it became clear for some of us that the key to create intelligent robots consisted of letting them evolve, self-organize, and adapt to their environment in order to survive and reproduce, just like all life forms on Earth have done and keep doing.The name Evolutionary Robotics was coined to define the collective effort of engineers, biologists, and cognitive scientists to develop artificial robotic life forms that display the ability to evolve and adapt autonomously to their environment.In this chapter we will show how we can evolve physical robots and describe some examples of the intelligence that these robots develop.However, the dominant view by mainstream engineers that robots were mathematical machines designed and programmed for precise tasks, along with the technology available at that time, delayed the realization of the first experiments in Evolutionary Robotics for almost ten years.In the spring of 1994 our team at EPFL, the Swiss Federal Institute of Technology in Lausanne (Floreano and Mondada, 1994) and a team at the University of Sussex in Brighton (Harvey, Husbands, and Cliff, 1994) reported the first successful cases where robots evolved with minimal human intervention and developed neural circuits allowing them to autonomously move in real environments.The two teams were driven by similar motivations.On the one hand, we felt that a designer approach to robotics was inadequate to cope with the complexity of the interactions between the robot and its physical environment as well as with the control circuitry required for such interactions.Therefore, we decided to tackle the problem by letting these complex interactions guide the evolutionary development of robot brains subjected to certain selection criteria (technically known as fitness functions), instead of attempting to formalize the interactions and then designing the robot brains.On the other hand, we thought that by letting robots autonomously interact with the environment, evolution would exploit the complexities of the physical interactions to develop much simpler neural circuits than those typically conceived by engineers who use formal analysis methods.We had plenty of examples from nature where simple neural circuits were responsible for apparently very complex behaviours.Ultimately, we thought that Evolutionary Robotics would not only discover new forms of autonomous intelligence, but also generate solutions and circuits that could be used by biologists as guiding hypotheses to understand adaptive behaviours and neural circuits found in nature.