Can AI systems culturally evolve their knowledge?

Abstract

Many artificial intelligence systems are developed which interact with human beings and other such systems, sometimes embodied as robots. Their behaviour is driven by the knowledge they have on their environment. This knowledge may be learnt from experience. This is actually what advertised machine learning systems do. However, such systems will only behave properly if this knowledge, or culture, is shared with others. This raises the question: can they socially evolve their knowledge as assumed in cultural evolution? Answering this question calls for developments in computational cultural (knowledge) evolution. It is at a crossroad between two threads: - Cultural knowledge evolution in which cultural evolution is applied to the knowledge and beliefs of organisms. - Computational cultural evolution in which computer capabilities are used to synthetise and analyse cultural evolution. It may be both experimental in which short-term small experiments are performed, or observational, in which observations can be made in the long run on large populations. Computational cultural knowledge evolution may actually cover two types of activities: Agent-based simulation of cultural evolution in which agents are a model that can predict how a modelled system works or Artificial cultural evolution in which programs or agents are new `organisms' exhibiting cultural evolution. Both activities are worth pursuing to study the principles of cultural (knowledge) evolution. For instance, the discussion about other species failing to develop cumulative culture supports the conclusion that the human species has a unique feature: cumulativity. However, it may be more simply related to the capability to deal with complex cultural items which support cumulativity better or to rely on more elaborate adaptation mechanisms that provide this cumulativity. So far, it is not possible to decide. Similarly, it is difficult to provide direct evidence of the absence of replicator. Developing agents able to evolve cumulatively their culture could bring some insight in these debates. We are experimenting with agents learning their knowledge from their environment and from other agents. They behave according to this knowledge and are able to adapt their knowledge in order to succeed in social games exploiting it. The advantage of using agents bearing an explicit representation of knowledge is that it is possible to assess the evolution of the properties of agents' knowledge. In particular, the accuracy, the consistency, or the diversity of the knowledge can be directly measured.