A computational model of an intuitive reasoner for ecosystem control
Intuition is the human capacity to make decisions under novel, complex situations where knowledge is incomplete and of variable levels of certainty. We take the view that intuition can be modeled as a rational and deductive mode of information processing which is suited to novel, complex situations. In this research, a computational algorithm, or intuitive reasoner, is proposed which mimics some aspects of human intuition by combining established mathematical tools, such as fuzzy set theory, and some novel innovations. A rule-based scheme is followed and a rule-learning module that allows rules to be learned from incomplete datasets is developed. The input and the rules drawn by the reasoner are allowed to be fuzzy, multi-valued, and low in certainty. A measure of the certainty level, Strength of Belief, is attached to each input as well as each rule. Solutions are formulated through iterations of consolidating intermediate reasoning results, during which the Strength of Belief of corroborating intermediate results is combined. An experimental implementation of the proposed intuitive reasoner is reported, in which the reasoner was used to solve a classification problem. The results showed that, when given increasingly sparse input data, the rule-learning module generated more rules of lower associated certainty than when presented with more complete data. The intuitive reasoner was able to make use of these low-certainty rules to solve the classification problems with an accuracy that compared favorably to that of traditional methods based on complete datasets.
intuition artificial intelligence knowledge acquisition limited certainty