Editor: On "Learning Language" I was dismayed by the inclusion of William Katke's article ("Learning Language Using A Pattern Recognition Approach," Spring 1985). Usually you do an excellent job of representing "the current state of the art in Artificial Intelligence" (to quote your Editorial Policy), but I consider this article an exception. First of all, although the article claims to be on "Learning Language," what it presents is at best a knowledge-free approach to learning syntax. I saw no evidence that the induced syntax is useful for anything, and good reasons to believe that it is not, such as the unmnemonic category names and the intrinsic limitations of finite state grammars. Second, this kind of stuff has been done before, and it didn't work too well then either; for a useful overview of the field and pointers into the literature, see the article on "Grammatical Inference" in Volume 3 of The Handbook of The plete specifications and the verification of proposed impleideas and issues presented were firmly focused on a conven-mentations, we should concentrate more on incremental tional view of the design process-a view I can caricaturize development of specifications as a result of assessment of as the SPIV methodology: performance.

The Seventh International Conference on Intelligent User Interfaces (IUI-2003) was held 12-15 January 2003 in Miami Beach, Florida. The conference brought together researchers and practitioners to report on outstanding research and applications, examine emerging work, and delineate new avenues for intelligent user interfaces. The conference received an all-time record number of submissions, covering a wide range of areas and approaches. Oral presentation sessions were organized into seven major topics: (1) adaptive and collaborative interfaces, (2) affective interfaces, (3) agent-based interfaces, (4) knowledge acquisition and visualization, (5) model-based interface design, (6) multimodal input, and (7) natural language interfaces. The conference program included three invited talks, each reflecting a different direction for developing the intelligent interfaces of the future.

Hence, at a coarse-grained level of abstraction, KB-Ss can be characterized in terms of two components: (1) a knowledge base, encoding the knowledge embodied by the system, and (2) a reasoning engine, which is able to query the knowledge base, infer or acquire knowledge from external sources, and add new knowledge to the knowledge base. A knowledge-level account of a KBS (that is, a competencecentered, implementation-independent description of a system), such as Clancey's (1985) analysis of first-generation rule-based systems, focuses on the task-centered competence of the system; that is, it addresses issues such as what kind of problems the KBS is designed to tackle, what reasoning methods it uses, and what knowledge it requires. In contrast with task-centered analyses, Levesque and Lakemeyer focus on the competence of the knowledge base rather than that of the whole system. Hence, their notion of competence is a task-independent one: It is the "abstract state of knowledge" (p. This is an interesting assumption, which the "proceduralists" in the AI community might object to: According to the procedural viewpoint of knowledge representation, the knowledge modeled in an application, its representation, and the associated knowledge-retrieval mechanisms have to be engineered as As a result, they would argue, it is not possible to discuss the knowledge of a system independently of the task context in which the system is meant to operate.

They thought that the Chinese Room argument showed that computationalism could never fully account for the first-person perspective, that the "computer metaphor for the mind" might lead to some vital social questions being ignored, that passing the Turing Test They conducted 20 interviews with a rather idiosyncratic collection of people, largely on the east and west coasts, to find out what the consensus was in the field. One of their happy discoveries was that connectionism (about which they initially knew little) was expected to overcome many of these obstacles. Each interview begins with a brief personal history of why the interviewee became involved with the subject and what they take it to be, and then moves into a discussion of contemporary issues which the editors find interesting. While the interviews do not conform to a set pattern, they return regularly to a few favorite themes: the Chinese Room, the importance of the Turing Test, why "symbolic AI" has failed (a claim that is made repeatedly throughout the book), and the significance of connectionism as a replacement for it Wilensky, and Winograd could possibly be said to be active in mainstream AI; on the other hand there are seven or eight philosophers, of whom only Dennett has a sympathetic interest in AI; all the others have rejected its premises, and Dreyfus, Searle and Weizenbaum are notorious for their passionate and sustained attacks on the subject. This would be less important but for the fact that AI is the main subject matter of several of the interviews.

Like most consultants, we have developed certain paradigms that we use to help our clients. We have worked with both entrepreneurs starting small technologyoriented business and with sources of venture capital. Frequently, we find that there is a gap between these two groups created by vastly different goals and objectives as well as diverse communication styles. The unfortunate result of this gap is the difficulty many startups experience in obtaining capital. We do not intend to provide a comprehensive review of business theory, to contrast our methodology with others, or to provide a historical perspective on venture capital.

Johanna Moore's work in the area of computer-generated explanation has been highly influential. Her thesis work, as well as the subsequent work of her and her students, has helped to change the way we think about the problem of generating explanations. The crux of the explanation problem, according to Moore, is not how to present information as such but how to impart an understanding on the user. The explanation system should be flexible enough that if an initial explanation fails to convey the understanding, it can try explaining the concept in a different way. The system should be aware of what it previously said to the user and what its communicative goals were at the time.

This advertisement might be posted by any manager delegatcd the responsibility for investigating the applications and market possibilities of expert systems for his/her company . To the rescue have come the authors whose books are reviewed in this article. Each author provides answers to some of the questions raised by those considering the use of expert systems on microcomputers: What are expert systems? Can they be implemented on a PC? Have any successful PC applications been created? Do I really need an expert system?

In this respect, what Pearl seems to have accomplished sometimes looks like a formalism in search of an interpretation without which the truth or the falsity of his claims is often impossible to assess. If the conceptions upon which his view is based do indeed conform to one or another of the traditional Bayesian models, moreover, then the very idea of a probability-based heuristic confronts a number of difficult problems of its own with respect to the distribution of probabilities to sets of alternative hypotheses, paths, or solutions, relative to the proposed refinements of those alternative hypotheses, paths, or solutions.6 These considerations suggest that traditional conceptions should not be taken for granted, especially if we assume that this is what Pearl intends by his observation that "Probability theory is today our primary (if not the only) language for formalizing concepts such as "average" and "likely," and therefore it is the most natural language for describing those aspects of (heuristic) performance that we seek to improve" (p. On general theoretical grounds, I think, there are excellent reasons to suppose that (a)-(f) are fundamental problems in AI science and that an extensional probabilistic analysis of this sort simply cannot lead to their effective solutions. In order to understand the traditional approach, however, this book is recommended with the reservations implied above, namely, that the author has omitted basic definitions that might not be familiar to some readers, and that serious difficulties seem to confront the theoretical framework he apparently endorses, where these difficulties are especially severe from an epistemological perspective.

The Second International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-03) was held in Melbourne, Australia, in July 2003. Attracting nearly 500 delegates, the event confirmed AAMAS as the academic main event for researchers with an interest in multiagent systems. We summarize the conference highlights and report on the associated workshops, tutorials, and emerging trends. Although a number of workshops had been held more or less regularly since 1980 (notably the U.S.-based Distributed Artificial Intelligence workshop series), until the mid-1990s, there was no dedicated archival venue for agentrelated work. By 2000, the situation had changed dramatically; by then, there were two major conferences, a major international workshop, and a dedicated journal, all publishing work in the agents area. Although all these venues were doing good business (there was no shortage of submitted papers), the overheads involved in organizing three major events--not to mention the ...

Should Artificial Intelligence strive to model and understand human cognitive and perceptual systems? Should it operate at a more abstract mathematical level of characterizing possible intelligent action, independent of human performance? Or, should it focus on building working programs that exhibit increasingly expert behavior, irrespective of theoretical or psychological conccrlls? These questions lie at the heart of most current, debate on whether AI is a science, an art, or a new branch of engineering In fact, some researchers believe it is all three and consequently build systems that perform some interesting task, arguing for the "theoretical significance" and "psychological validity" of the approach. In fact, it assumes the cognitive psychology paradigm as central and suggests that AI research would benefit from closer adherence to the data and methods of psychological research We welcome contributions in support of other research methodologies in AI, as well as discussions com-Rcscarch for this paper was conducted at the LJniversity of Chicago Center for Cognitive Science under a grant.