An online incremental learning pattern-based reasoning system
An architecture for reasoning with pattern-based ifthen rules is proposed. By processing patterns as real-valued vectors and classifying similar ifthen rules into clusters in long-term memory, the proposed system can store pattern-based ifthen rules of propositional logic, including conjunctions, disjunctions, and negations. Moreover, it achieves some important properties for intelligent systems such as incremental learning, generalization, avoidance of duplicate results, and robustness to noise. Results of experiments demonstrate that the proposed method is effective for intelligent systems for solving various tasks autonomously in a real environment.
incremental learning pattern-based reasoning clustering real world environment