Computer Science and Artificial Intelligence

Abstract

This chapter takes a step back to place our problem of Natural Language Learning in the historical and theoretical perspectives of Artificial Intelligence. For this purpose we distinguish this engineering tradition of AI from the empirical approaches of Cognitive Science. This then provides a bridge to those techniques and theoretical stances which are not grounded in Linguistics, Psychology or Neurology. The Turing Test provides a foundational metric for AI which focuses our understanding of the term intelligence on this language and learning facility. However, the methodological focus of engineered intelligence is the application of heuristics in a search paradigm. Since virtually any problem can be set up as a problem of choosing an appropriate path through a decision tree, this is an appropriate base for AI. Expert Systems and Natural Language systems traditionally have programmed deterministic rules which largely eliminate the search component and reduce the generality of the systems. But the addition of a general Problem Solving or Machine Learning component returns such systems to the fold of heuristic search. Such systems differ in the way they are read, taught and criticized, how they obtain and make use of positive and negative examples, and in the ex-tent to which they are expected to be error-free and intuitive.