Utilizing recurrent network topologies to produce case/role meaning representations for single sentences has become common practice in connectionist natural language processing systems. Typically, these systems train with the complete sentence meaning as the target output for the entire period that the sentence is being processed; i.e., the complete meaning is available starting with the first word of the sentence. Thus, the context feedback provided by these systems is non-incremental in that they use information about the sentence that has not yet been encountered in order to aid in the processing and learning tasks. SAIL1 is a connectionist natural language processing system which builds the sentence meaning representation incrementally, incorporating into the meaning only the information derived from words already processed.

We describe an approach to deriving efficient rules for interpreting the intended meaning of indirect speech acts. We have constructed a system called SALLY that starts with a few, very general principles for understanding the intention of the speaker of an utterance. After inferring the intended meaning of a particular utterance, SALLY creates a specialized rule to understand directly similar utterances in the future.

Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" - dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction.

Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" — dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction.