AI. Specifically, we address three Just how little can be illustrated by the reaction to Winograd and Flores's (1986) recent book Understanding Computers and Cognition. In personal comments, the book and its authors have been savaged. Published comments are, of course, more temperate (Vellino et al. 1987) but still reveal the hypersensitivity of the Penrose's (1989) even more recent book The Emperor's New Mind have been observed. Like Suchman (1987) and Clancey (1987), we feel that insights of significant value are to be gained from an objective consideration of traditional and alternative perspectives. Some efforts in this direction are evident (Haugeland [1985], Hill [1989], and Born [1987], for example), but the issue requires additional and ongoing attention.

However, recently, there seems to be a new wave of interest, as indicated by many papers, monographs, edited books, and doctoral theses, in exploring aspects of similarity and analogical reasoning from various perspectives. Amid these numerous publications, Similarity and Analogical Reasoning surely stands out as the most valuable reference work on the topic, covering especially well the recent advances in the understanding of this topic, with many chapters written by leading researchers. Although it is based on a collection of papers initially presented at the Workshop on Similarity and Analogy, unlike the typical workshop proceedings, this volume is well edited and coherent in both its content and format, with a great deal of cross-references and detailed summary-comment chapters for every part of the book. Let us look at the book in detail. Because each of these chapters has a different perspective, approach, and organization, I first discuss a number of chapters one by one.

As a field, knowledge representation has often been accused of being off in a theoretical noman's land, removed from, and largely unrelated to, the central issues in AI. This article argues that recent trends in KR instead demonstrate the benefits of the interplay between science and engineering, a lesson from which all AI could benefit. This article grew out of a survey talk on the Third International Conference on Knowledge Representation and Reasoning (KR '92) (Nebel, Rich, and Swartout 1992) that I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI '93). This article is an edited version of a talk surveying that conference, which I presented at the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI '93). Although nominally a conference overview, the article attempts to summarize the state of the conference and the field with respect to the intertwined goals of science and engineering.

The fifth Distributed Artificial Intclligencc Workshop tias held at the Schlumberger-Doll Research Laboratory from October 14 to 17, 1984 It was attended by 20 participants from academic and industrial institutions. As in the past,' this workshop was designed as an informal meeting It included brief research rcport,s from individual groups along with genera1 discussion of questions of common interest. Distributed artificial intelligence (DAI) is concerned with cooperative solution of problems by a decentralized and loosely coupled collection of knowledge sources (KSs), each embodied in a distinct processor node. The KSs cooperate in the sense that no one of them has sufficient information to solve the entire problem; mutual sharing of information is necessary to allow the group as a whole to product an answer. By decentralized we mean that both control and data are logically and often geographically distributed; there is neither global control nor global data storage.

To fully appreciate Professor Pearl's book, begin with a careful reading of the title. It is a book about "..Intelligent- ..Strategies.." for the discovery and use of "Heuristics.. " to allow computers to solve ".. Search.. ' ' problems. Search is a critical component in AI programs (Nilsson 1980, Barr and Feigenbaum 1982), and in this sense Pearl's book is a strong contribution to the field of AI. It serves as an excellent reference for the researcher/practitioner and is useful as a textbook as well. As a book about search, it is thorough, at the state of the art, and contains expositions that will delight the expert with their clarity and depth.

The Programmer's Apprentice project uses the domain of programming as a vehicle for studying (and attempting to duplicate) human problem solving behavior. Recognizing that it will be a long time before it is possible to fully duplicate an expert programmer's abilities, the project seeks to develop an intelligent assistant system, the Programmer's Apprentice (PA), which will help a programmer in various phases of the programming task. The Knowledge-Based Editor in Emacs (KBEmacs) is an initial step in the direction of the PA. A question that has been asked about KBEmacs is, "Where's the AI?" Going beyond this, the article uses the development of KBEmacs as an example that illustrates a number of general features of the process of developing an applied AI system. As part of this, the article compares the way AI ideas are used in KBEmacs with the way they were used in the initial proposal for the PA.

AAAI: It's Time for Large-Scale Systems The most important challenge facing AI today is enabling components to interact in larger scale systems, where modules built with multiple alternative methodologies can be incorporated into robust applications. The infrastructure--computing power, memory, bandwidth, and connectivity--has evolved dramatically. Important theoretical advances have been made in areas such as machine learning, natural language, knowledge representation, task descriptions, sensing, and action in the world. Once again there is substantial demand for AI applications from customers such as DARPA, with a requirement to solve real problems. We need to find ways making AI components interact in larger scale systems.

There has been a flurry of interest recently in the possibility of standardizing existing work on knowledge representation; this interest is supported by the Defense Advanced Research Projects Agency (DARPA) and other funding agencies. An examination of recent work on knowledge representation makes it clear that there are deep differences among the approaches taken. Those supporting knowledge representation standards are attempting to address this difficulty by creating a single language in which all knowledge representation schemes can be expressed (Genesereth 1990), but this task seems impossible given the current state of the field. However, it is surely not possible to construct a language that will also incorporate all future knowledge representation work, other than in the trivial sense guaranteed by the universality of some specific method, such as first-order logic or a general-purpose programming language. Furthermore, attempts in this direction will inevitably constrain future knowledge representation efforts; even gentle constraints might have a stifling impact on future knowledge representation work.

The 1994 Workshop on Computational Dialectics was held during the 1994 National Conference on AI. At issue were the following ideas: (1) dialectic has something to do with computation, (2) AI has something new to contribute to the understanding of dialectic, and (3) dialectic approaches to longstanding AI problems permit new progress. This article outlines the significant research presented at the conference. It is to his credit that the idea has earned a further hearing in a German workshop that he is currently organizing with colleagues on the continent. For the workshop in Seattle, Washington, at AAAI-94, our committee included Johanna Moore (Pitt) and Katia Sycara (Carnegie Mellon University).

To be considered exceptional, a textbook must satisfy three basic requirements. First, it must be authoritative, written by one with a broad range of experience in, and knowledge of, a subject. Second, it must effectively communicate to the reader, in the same manner in which a course instructor must be capable of imparting knowledge to students in a classroom. Third, it must stimulate the reader into thinking more deeply about the subject and into viewing it from fresh perspectives. In Artificial Intelligence: A Knowledge-Based Approach (Boyd & Fraser, Boston, 740 pp., $48.95), author Morris W. Firebaugh has succeeded in meeting each of these requirements.