What Is a Knowledge Representation? Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it--What is it?--has Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have.

What Do We Know about Knowledge? However, the simple equation "knowledge is power" leaves three major questions unanswered. First, what do we mean by "knowledge"; second, what do we mean by "power"; and third, what do we mean by "is"? In this article, I will examine the first of these questions. In particular I will focus on some of the milestones in understanding the nature of knowledge and some of what we have learned from 50 years of AI research. The discipline and detail required to write programs that use knowledge have given us some valuable lessons for implementing the knowledge principle, one of which is to make our programs as flexible as we can. Many of them are well known, but they can serve as reminders of the difficulty of implementing the "knowledge is power" principle. I wish to clarify the knowledge principle and try to increase our understanding of what programmers and program designers need to do to make the knowledge principle work in practice. The "knowledge is power" principle is most closely associated with Francis Bacon, from his 1597 tract on heresies: "Nam et ipsa scientia potestas est." ("In and of itself, knowledge is power.") Bacon was among the first of the modern philosophers to separate the concept of scientific knowledge from knowledge gained through the two dominant methods for attaining truth in his time: magic and religious revelation. The essential difference for him, as for us, is that knowledge gained through experiment is replicable by others. Although all the empirical sciences rely on the replication of observations and experiments, AI has been slow to embrace this principle. Programs demonstrating research ideas in AI are often too large and not well enough documented to allow replication or sharing. Applications programs, however, are designed to be used by others outside the research lab and thus are more amenable to multiple runs in diverse conditions. Thus they have the potential to provide experimental data demonstrating strengths, weaknesses, and benefits. The knowledge principle predates Bacon. For example, it was pretty clearly articulated in Biblical times: "A man of knowledge increaseth strength" (Proverbs 24: 5). Socrates, Plato, Aristotle, and other early Greek philosophers based their lives on acquiring and transferring knowledge. In the course of teaching, they sought to understand the nature of knowledge and how we can establish knowledge of the natural world. Socrates is famous for pointing out the value of knowledge and seeking truth, as in "… that which we desire to have, and to impart to oth-

This article, derived from the 1996 American Association for Artificial Intelligence Presidential Address, explores the notion of intelligence from a variety of perspectives and finds that it "are" many things. It has, for example, been interpreted in a variety of ways even within our own field, ranging from the logical view (intelligence as part of mathematical logic) to the psychological view (intelligence as an empirical phenomenon of the natural world) to a variety of others. One goal of this article is to go back to basics, reviewing the things that we, individually and collectively, have taken as given, in part because we have taken multiple different and sometimes inconsistent things for granted. I believe it will prove useful to expose the tacit assumptions, models, and metaphors that we carry around as a way of understanding both what we're about and why we sometimes seem to be at odds with one another. Intelligence are also many things in the sense that it is a product of evolution.

Various techniques were presented for eliciting the decision model incrementally in conjunction with the problem-solving process. Well-established techniques from decision analysis, including sensitivity analysis and value of information calculation, were also discussed in the context of incremental model elicitation. Finally, the importance of selfexplanatory systems was emphasized because the user needs to understand the impact of his/her communicated preferences and their role in the problem-solving process. The American Association for Artificial Intelligence Spring Symposium on Interactive and Mixed-Initiative Decision-Theoretic Systems was held at Stanford University from 23-25 March 1998. The symposium attracted approximately 30 researchers from around the world.

Problem-solving techniques such as modeling, simulation, optimization, and network analysis have been used extensively to help agricultural scientists and practitioners understand and control biological systems. By their nature, most of these systems are difficult to quantitatively define. Many of the models and simulations that have been developed lack a user interface which enables people other than the developer to use them. As a result, several scientists are integrating knowledgebased-system (KBS) technology with conventional problem-solving techniques to increase the robustness and usability of their systems. To investigate the similarities and differences of leading scientists' approaches, a pioneer workshop, supported by the American Association for Artificial Intelligence (AAAI) and the Knowledge Systems Area of the American Society of Agricultural Engineers, was held in San Antonio, Texas, on 10-12 August 1988.

Inquire Biology provides unique capabilities through a knowledge representation that captures conceptual knowledge from the textbook and uses inference procedures to answer students' questions. Students ask questions by typing free-form natural language queries or by selecting passages of text. The system then attempts to answer the question and also generates suggested questions related to the query or selection. The questions supported by the system were chosen to be educationally useful, for example: what is the structure of X? compare X and Y? how does X relate to Y? In user studies, students found this question-answering capability to be extremely useful while reading and while doing problem solving. In an initial controlled experiment, community college students using the Inquire Biology prototype outperformed students using either a hard copy or conventional ebook version of the same biology textbook. While additional research is needed to fully develop Inquire Biology, the ...

It is the ultimate scientific question underlying psychology and AI as well as a substantial part of philosophy: What is the nature of the mind? Newell's autobiography (American Psychological Association 1986) puts it thusly: The central line of Newell's research has remained always the quest for understanding the nature of the mind. The detailed analysis of protocols, the development of production systems, pulling together the theory of human problem solving (in the book of the same name, with Herb Simon), the development of the notion of cognitive architecture, the problem-space hypothesis, a theory of how humans acquire cognitive skills, work on artificial intelligence systems for doing demanding intellectual tasks (such as discovering algorithms), the development of a complete architecture for intelligence--these are some of the main stepping stones. They comprise various mixtures of artificial intelligence and cognitive psychology, as chance and opportunity would have it. This central question will occupy Newell for the rest of his research life, no doubt.

Raymond Reiter, a professor of computer science at the University of Toronto, a fellow of the Royal Society of Canada, and winner of the International Joint Conference on Artificial Intelligence 1993 Outstanding Research Scientist Award, died September 16, 2002, after a yearlong struggle with cancer. Reiter, known throughout the world as "Ray," made foundational contributions to artificial intelligence, knowledge representation and databases, and theorem proving. Reiter, known throughout the world as "Ray," made foundational contributions to artificial intelligence, knowledge representation and databases, and theorem proving. Ray was born in Toronto, Canada, in 1939 to immigrant parents who came from Poland. He received a B.S. in mathematics from the University of Toronto in 1961 and an M.S. degree in mathematics in 1963 from the University of Toronto.

The fall of 2002 marked the passing of Ray Reiter, for whom a memorial article by Jack Minker appears in this issue. As the issue was going to press, AI lost Saul Amarel, Norm Nielsen, and Charles Rosen. We thank Tom Mitchell and Casimir Kulikowski for their memorial to Saul Amarel, Ray Perrault for his remembrance of Norm Nielsen, and Peter Hart and Nils Nilsson for their tribute to Charles Rosen. The AI community mourns our lost colleagues and gratefully remembers their contributions, which meant so much to so many and to the advancement of artificial intelligence as a whole. The foundation of Charlie's creativity was his broad knowledge.

This supposedly makes representing exceptions (three-legged elephants and the like) easy; but, alas, it makes one crucial type of representation impossiblethat of composite descriptions whose meanings are functions of the structure and interrelation of their parts. This article explores this and other ramifications of the emphasis on default properties and "typical" objects. While I believe this to be an important point, this article was never meant to be the definitive work on logical distinctions in knowledge representation. Some of the notions mentioned here in passing (e.g., analyticity) are perenially problematic. In addition, I have not really attempted to bring the body of the article up to date from its original form. The article is also generally nonconstructive. However, there is now ample evidence that this kind of analysis can lead to constructive suggestions for knowledge representation systems. In work pursued after the original version of this article was written, some ...