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.

It presents experimental studies of evolving low-level and high-level functions in real and simulated robots. The main thesis of the book is that evolutionary robotics provides a viable alternative to classical techniques of designing autonomous robots. An important point emphasized throughout the book is that "evolutionary robotics, … releases the designer from the burden of deciding how to break the desired behavior down into simple behaviors." The book is suited for both the educated reader with an interest in such matters and the professional reader, including researchers in artificial life, cognitive science, adaptive behavior and AI. Its style, scope, and depth make the reading worthwhile.

This article gives an overview of current research on animated pedagogical agents at the Center for Advanced Research in Technology for Education (CARTE) at the University of Southern California/Information Sciences Institute. Animated pedagogical agents, nicknamed guidebots, interact with learners to help keep learning activities on track. They combine the pedagogical expertise of intelligent tutoring systems with the interpersonal interaction capabilities of embodied conversational characters. They can support the acquisition of team skills as well as skills performed alone by individuals. At CARTE, we have been developing guidebots that help learners acquire a variety of problem-solving skills in virtual worlds, in multimedia environments, and on the web.

Such creative software can be used for autonomous creative tasks, such as inventing mathematical theories, writing poems, painting pictures, and composing music. However, computational creativity studies also enable us to understand human creativity and to produce programs for creative people to use, where the software acts as a creative collaborator rather than a mere tool. Historically, it's been difficult for society to come to terms with machines that purport to be intelligent and even more difficult to admit that they might be creative. For instance, in 1934, some professors at the University of Manchester in the United Kingdom built meccano models that were able to solve some mathematical equations. Groundbreaking for its time, this project was written up in a piece in Meccano Magazine.

The Journal of Artificial Intelligence Research (JAIR) was one of the first scientific journals distributed over the web. It has now completed over five years of successful publication. Electronic publishing is reshaping the way academic work is disseminated, and JAIR is leading the way toward a future where scientific articles are freely and easily accessible to all. This report describes how the journal has evolved, its "grassroots" philosophy, and prospects for the future. By August, JAIR had reviewed, accepted, and published 2 articles and rejected 18 others.