Evolution of Altruism in Robots
Posted on May 5, 2011 Comments (1)
The webcast explores robots evolving cooperative behavior. A Quantitative Test of Hamilton’s Rule for the Evolution of Altruism (open access paper)
By conducting experimental evolution over hundreds of generations of selection in populations with different costs and benefits of altruistic behavior, we show that kin selection theory always accurately predicts the minimum relatedness necessary for altruism to evolve. This high accuracy is remarkable given the presence of pleiotropic and epistatic effects, as well as mutations with strong effects on behavior and fitness. In addition to providing a quantitative test of kin selection theory in a system with a complex mapping between genotype and phenotype, this study reveals that a fundamental principle of natural selection also applies to synthetic organisms when these have heritable properties.
Related: Robots That Start as Babies Master Walking Faster Than Those That Start as Adults – Friday Fun: Robocup 2010, Robot Football – Toyota Develops Thought-controlled Wheelchair
An earlier experiment by the same lab explored Evolution of Cooperation in Artificial Ants
During the first stage of experiments, we implemented and used a very fast probabilistic simulator to conduct an initial analysis of these four cases. In this simulation, decisions were not based on individual controllers, but instead on a probabilistic model. Probabilities were estimated in test runs with the real robotic setup. The advantage of such an approach is its simplicity and speed, allowing for a fast evaluation of the parameter ranges and yielding a very complete picture of the space of possible solutions…
In the second stage, we have finished the implementation of a physics-based, 2D simulator (available online here). Work on the simulator was conducted in cooperation with the ECAgents project. As opposed to the probabilistic simulator, decision making was based on individual controllers, which allowed a transfer of behavior evolved in simulation to the real Alice micro-robots. Direct evolution of the desired behaviors on real robots is far too time consuming for tasks with a high number of parameters. Benchmarks of our simulator at the start of this project showed that its simulation speed compared very favorably to commercially available alternatives less adapted to the specific needs of our project.