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.

If you are interested in writing a review, contact chandra@cis. It is intended to be a "general textbook of knowledge-base analysis and design" (p. Its great strength is recognizing the need for an interdisciplinary approach, and the attempt at presenting the logical and philosophical foundations of knowledge representation under a unified view. Its great weakness is a lack of consistent rigor, which is needed in a textbook for newcomers to a subject. After some historical remarks and a first introductory chapter devoted to logic, Sowa immediately attacks the hard problems involved in choosing ontological categories, which lie at the heart of any knowledge representation project.

Is reading Herb Simon's delightful autobiography worth boarding the wrong commuter train? Written in an informal style, Models of My Life presents a lively and insightful self-portrait of this father of AI. In keeping with his character, Simon uses the metaphor of the maze to describe his life: "In describing my life as mazelike, I do not mean that I have made a large number of deliberate, wrenching decisions to go off in one direction or another. On the contrary, I have made very few. Obvious responses to opportunities and circumstances, rather than studied decisions, have put me on the particular roads I have followed" (pp.

The notion of generic design, although it has been around for 25 years, is not often articulated; such is especially true within Newell and Simon's (1972) informationprocessing theory (IPT) framework. Design is merely lumped in with other forms of problem-solving activity. Intuitively, one feels there should be a level of description of the phenomenon that refines this broad classification by further distinguishing between design and nondesign problem solving. However, IPT does not facilitate such problem classification. This article makes a preliminary attempt to differentiate design problem solving from nondesign problem solving by identifying major invariants in the design problem space.