Recognizing Time-series Data by Generating Agents of Inner-class Model

Abstract

The inner-class model is introduced as a basic principle of artificial intelligence. More flexible, human-like, and context-based performance is expected for this model, such as with respect to recognition, control, and problem solving. The only assumption of this model is that a feature corresponding to the co-occurrence of two features is generated by just pairing the two features. An arbitrary feature created by this assumption is called an agent. This paper describes how the model 1) records and reproduces time-series data, 2) sets and reaches goals, 3) creates new concepts, and 4) deletes useless agents. The results of experiments are also shown to verify that the model is able to record and reproduce a simple time-series of data, and that the model reaches a given goal.