ABSTRACT
Artificial intelligence has traditionally favored a reductionistic approach, studying intelligent behavior by analyzing each component independently: Knowledge representation, search-intensive problem solving, knowledge-intensive expertise, concept acquisition from examples, performance improvement due to experience, and so forth. Such a divide-and-conquer approach has been historically quite appropriate, providing many useful results. However, artificial intelligence is evolving from an exploratory endeavor to a quantitative science, and part of the maturation process is the emergence of unifying theories and integrated computational architectures. This chapter describes one such investigation, the PRODIGY system, an integrated architecture unifying problem solving, planning and multiple learning methods. The learning methods in PRODIGY encompass learning control rules through explanation-based learning (EBL) and static search–space analysis, learning plan knowledge through analogical transfer, learning abstraction hierarchies through domain–definition analysis, and acquiring new domain knowledge through goal-oriented experimentation and dynamic interaction with a human expert.
