Representing inference control by hypothesis-based association

An approach for representing inference control is presented. It is proposed that the representation of inference control should consist of two levels: planning level which realizes problem solving strategies, and a performing level, which represents inference tactics. Based on this approach, the representation system hypothesis-based associative representation (HAR) has been developed to realize the functional architecture for knowledge-based systems. Because users are allowed to organize hypothesis-based associative networks that perform the problem solving strategies with different features, HAR becomes not only a tool for building knowledge-based systems, but also an environment for exploring AI techniques. For example, by comparing three strategies of block-world action planning, it is found that the least commitment strategy is the most efficient