Artificial intelligence,language, and the study of knowledge

This paper studies the relationship of Artificial Intelligence to the study of language and the representation of the underlying knowledge which supports the comprehension process. It develops the view that intelligence is based on the ability to use large amounts of diverse kinds of knowledge in procedural ways, rather than on the possession of a few general and uniform principles. The paper also provides a unifying thread to a variety of recent approaches to natural language comprehension. We conclude with a brief discussion of how Artificial Intelligence may have a radical impact on education if the principles which it utilizes to explore the representation and use of knowledge are made available to the student to use in his own learning experiences.     

Rules and topics in conversation

Rules of conversation are given that specify what can follow what. A system for deciding what makes a reasonable subject for a conversation is shown. Topics are discussed and rules for topic shift are presented.     

Problem solving in semantically rich domains: An example from engineering thermodynamics

Recent research on human problem solving has largely focused on laboratory tasks that do not demand from the subject much prior, task-related information. This study seeks to extend the theory of human problem solving to semantically richer domains that are characteristic of professional problem solving. We discuss the behavior of a single subject solving problems in chemical engineering thermodynamics. We use as a protocol-encoding device a computer program called SAPA which also doubles as a theory of the subject's problem-solving behavior. The subject made extensive use of means-ends analysis, similar to that observed in semantically less rich domains, supplemented by recognition mechanisms for accessing information in semantic memory.     

Temporal order judgments with amnesia

The hypothesis that patients with anterograde amnesia fail to recognize the temporal relations among recognizable past events was evaluated. Experiment 1 showed that order recognition for words in a list was at chance levels even though event recognition was significantly above chance. The same pattern did not obtain for intact adults. Experiment 2 examined event and order recognition for news events. Amnesics' event recognition for news events was quite accurate and comparable to controls, yet their order recognition was significantly below that of controls, again at chance levels. It is argued that the failure to encode the temporal relations among events has broad consequences for memory performance in general.