Heuristics Learning System Based on Non-optimum Recognition

In this paper, we present extension learning (EL) a system for heuristics learning under non-optimum condition. The purpose of this study is to develop a new model for heuristics learning systems from human-computer cooperative perspective. We have established the theoretical foundation and conceptualization of the constructs for extension learning relationship with sub-optimum causal order, and sketch the skeleton of the mapping inversion on the extension relationship. Through the construction of the relationship between practical learning model and on-the-spot model, it sets up a couple of mapping models of learning process. The proposed learning model provides a framework for the design of heuristics learning systems from the practical perspective to enhance user self-learning and application of EL.