Modeling and Applications of Multi-Dimensional Interval Data to Artificial Life, Control Systems, Decision Making, etc

By using Interval data, passing it through a matrix and analyzing the results. It is possible to achieve a series of outcomes that will contribute to the cognitive processes of artificial life forms, control systems, intelligent probes and robots, decision-making aids, signposting, models and simulations. This is possible by using a matrix employing finite mathematics environments, fuzzy logic principles, granulated interval mathematics, clustering routines, Euclidean distance measures and multi dimensional inputs. This paper seeks to explore the outcomes of developing Nolan’s Matrix, and the contribution of interval mathematics to its operation, and a selection of examples, where the matrix has provided some interesting results.