Integrating Knowledge Sources for Computer "Understanding" Tasks

Abstract

Recent research in artificial intelligence has identified a number of knowledge sources which appear to be needed for effective automatic "understanding" of connected natural language speech and text. These include word-and phrase-level semantics, models for actors and objects, and inference techniques for using script- or goal-oriented knowledge structures. A unified model of the understanding process can be defined using the distributed-computing viewpoint, provided some way can be found to integrate and control a collection of "experts," each one associated with a certain kind of knowledge source. A technique is described, called hierarchical task management, for constructing computer language-processing systems comprising an arbitrary number of distinct, potentially distributed, processes. The technique is based upon the repeated activation and expansion of data structures called tasks, which define important components of the understanding process in terms of a controlled interaction among the experts. The tasks are maintained on several "agendas," and are manipulated by a uniform monitor called the task manager. The process of task management is illustrated in a multiprocess story understander called a distributable script applier mechanism (DSAM), which reads and summarizes newspaper stories about plane crashes.