AI—A multiple book review

Abstract

Textbooks on Artificial Intelligence offer a unique trace of the development of our discipline. The topics and the focus have been changing over the decades: The first AI book I put my hands on was [4] and astonishing enough, the latest AI book I received is from the same author, [6]. Nearly 30 years of research have led to a considerable difference between the two books. While the first one was dealing, as the title promises, with “Problem Solving Methods in Artificial Intelligence”, the latter is aiming at the introduction of a new synthesis, a new definition of our field. In the 1971 book (and in the 1980 book [5] on “Principles of Artificial Intelligence”) the main sections cover search, resolution, plan generation and structured object representation, i.e., a careful development of semantic networks starting from a predicate logic description. This aspect became more important with the success of knowledge based systems. And indeed the eighties, the high time of knowledge representation, led to AI books focusing on various knowledge representation formats. In [8] and [2] considerable parts deal with knowledge representation formats like frames, conceptual dependencies or semantic networks. There was a clear shift from general problem solving techniques, like heuristic search and inference, towards knowledge representation and its applications. The eighties witnessed the flourishing of AI—knowledge based systems and expert systems seemed to be the main contribution of AI to the rest of computer science and hence the various formats for representing knowledge have been considered to play an essential role. In the nineties this situation changed drastically: it turned out that a deep understanding of the various representation languages is only possible if they are mapped onto logical formalisms. But even more, it became obvious that logical semantics of semantic networks, frames and similar formats are the key to the analysis and design of efficient inference systems. As a consequence specialized logical systems became the main ingredients of the AI-toolbox. During the eighties, however, not only did we experience the high time of knowledge based system, at the same time “Nouvelle AI” entered the floor: bottom up, situated, embodied and following the premise that “Intelligence is in the eye of the observer” [1]. Nowadays, numerous groups are researching probabilistic approaches, neural networks or