Learning of an Anticipatory World-Model and the quest for General versus Reinforced Knowledge

The ultimate aim of AI is to understand intelligence in a constructive way, i.e., to build systems exhibiting intelligent behavior. An anticipatory system, i.e., a system having a model of itself and/or its environment (Rosen 1985), has obviously a “higher cognitive level” than a purely reactive system, as it is capable of planning, imitation, and so on. But from a “designer viewpoint”, i.e., as an AI researcher who actually has to build systems, the modeling is a hard and laborintensive task. For this reason among several others, e.g. to be able to cope with changing environments, it is tempting to propose learning as panacea, i.e., to build up and update the model while interacting with the world. When the world-model is build up a crucial question involved is the balance between general and reinforced knowledge, i.e., information that could be useful and information that has already been indicated as being profitable. A naive solution is to make a complete model, meaning to store as much information about the world as possible. But this is usually infeasible for reasons of computational complexity, especially in respect to memory and speed. Another problem is that some information about the world can only be found in an active way, i.e., through interaction with the world including trial and error manners. But this can be dangerous; harmful situations can be encountered that otherwise would not emerge.