Self-adaptive constructivism in Neural XCS and XCSF
Gerard D. Howard, Larry Bull, Pier Luca Lanzi · 2008
For artificial entities to achieve high degrees of autonomy they will need to display appropriate adaptability. In this sense adaptability includes representational flexibility guided by the environment at any given time. This paper presents the use of constructivism-inspired mechanisms within a neural learning classifier system which exploits parameter self-adaptation as an approach to realize such behaviour. The system uses a rule structure in which each is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the system. Further, the use of computed predictions is shown possible.