Handling incomplete information in an evolutionary environment

In this paper we address the problem of modeling creativity in Artificial Intelligence using a Genetic or Evolutionary based approach to computing, where the universe of discourse is represented as theories or programs in an extension to the Logic Programming language, which makes possible to handle incomplete or even contradictory information in an evolutionary environment. Indeed, we present a new insight for the construction of evolutive systems that combines the potential of the knowledge representation and reasoning mechanisms, present in the logic programming languages. Here, in an evolutionary setting, the candidate solutions to model the universe of discourse are seen as evolutionary logic programs or theories, being the test whether a solution is optimal based on a measure of the quality-of-information carried by those logical theories or programs. From a point of view of the process, the quality-of-information of the universe of discourse is assessed on the fly, being therefore possible to select the best logical theory or program that models it, in terms of the same time line.