Past approaches to the problem of inference have often concentrated on a particular type of knowledge structure (such as a script) and postlulated an algorithm tuned to process just that type of structure. The problem with this approach is that it is difficult to modify the algorithm when it comes time to add a new type of knowledge structure.
An alternative, unified approach is proposed. This approach is formalized in a computer program named FAUSTUS. The algorithm recognizes six very general classes of inference, classes that are not dependent on individual knowledge structures. Rather, the classes describe general kinds of connections between concepts. New kinds of knowledge can be added without modifying the algorithm. Thus, the complexity has been shifted from the algorithm to the knowledge base. To accommodate this, a powerful knowledge representation language named KODIAK is employed.
The resulting system is capable of drawing proper inferences (and avoiding improper ones) from a variety of texts, in some cases duplicating the efforts of other systems, and in other cases improving upon them. In each case, the same unified algorithm is used, without tuning the program specifically for the text at hand.