Carnegie Mellon University and then continued investigating issues in representation and reasoning as part of his research career for the last decade and a half. However, the engineers, as is their wont, have their own take and emphasis many faces: Its philosophical progress, instigated by the focus on on AI issues. Teaching engineering and animals, and its mathematical list gives some idea about how students interested in AI, especially face to formulating and analyzing concerns with application bring advances when they are taking courses along classes of algorithms that appear to be in theory, as has happened earlier with computer science students, presents effective in providing computers with in mathematics and physics. Many academic researchers have the difference in background and interest. For several decades, there has found that AI often elicits greater interest Also, when ideas are presented been another face to the field, a technological from fellow academics in engineering somewhat abstractly, the engineering one that provides tools for departments--many computer students might need to do extra work solving practical problems in various science departments are housed in in seeing how they might be applied domains. AI It would thus be great if there interaction with AI.

A few weeks after my appointment as Book Reviews editor, I started receiving a large number of books from AAAI, books that have been accumulating since the last reviewer stepped down. As I was going through them, I thought, "So many books, so few pages." AI Magazine is not a publication exclusively devoted to books, such as the New York Review or the weekly book review supplements of major newspapers. At best, it can devote a few pages each issue to book reviews. Also, it doesn't appear that frequently, just four issues a year. Given these constraints, how can the magazine best serve its readership?

We report on the spring 1992 symposium on diagrammatic representations in reasoning and problem solving sponsored by the Association for the Advancement of Artificial Intelligence. The symposium brought together psychologists, computer scientists, and philosophers to discuss a range of issues covering both externally represented diagrams and mental images and both psychology -- and AI-related issues. In this article, we develop a framework for thinking about the issues that were the focus of the symposium as well as report on the discussions that took place. We anticipate that traditional symbolic representations will increasingly be combined with iconic representations in future AI research and technology and that this symposium is simply the first of many that will be devoted to this topic.

We report on the spring 1992 symposium on diagrammatic representations in reasoning and problem solving sponsored by the Association for the Advancement of Artificial Intelligence. The symposium brought together psychologists, computer scientists, and philosophers to discuss a range of issues covering both externally represented diagrams and mental images and both psychology -- and AI-related issues. In this article, we develop a framework for thinking about the issues that were the focus of the symposium as well as report on the discussions that took place. We anticipate that traditional symbolic representations will increasingly be combined with iconic representations in future AI research and technology and that this symposium is simply the first of many that will be devoted to this topic.

I propose a task structure for design by analyzing a general class of methods that I call propose-critique-modify methods. The task structure is constructed by identifying a range of methods for each task. This recursive style of analysis provides a framework in which we can understand a number of particular proposals for design problem solving as specific combinations of tasks, methods, and subtasks. The analysis shows that there is no one ideal method for design, and good design problem solving is a result of recursively selecting methods based on a number of criteria, including knowledge availability.

I concentrate on this class of design 1989) that lays out the relation problems in this article. An example of an implicit function mapping from behavior to structure), typically in many engineering devices is safety: For conducted by means of a search or exploration example, a subsystem's role might only be in the space of possible subassemblies explained as something that prevents the of components. This accent on assembly is in leakage of a potentially hazardous substance, fact the origin of the frequent suggestion that and this function might never be explicitly design is a synthetic task. Only a vanishingly design specifications will usually mention a small number of objects in this space constitute number of constraints. The distinction even satisficing, not to mention optimal, between functions and constraints is hard to solutions. What is needed to make design formally pin down; functions are constraints practical are strategies that radically shrink on the behavior or properties of the device. However, it is useful to distinguish functions Set against the view of design as a deliberative from other constraints because functions are problem-solving process is the view of the primary reason that the device is desired. Artistic creations and weigh more than..."), the process of making scientific theories are often said by their creators the artifact from its description (manufacturability to have occurred to them in this Even when a plausible solution itself (for example, "I want a design within a occurs in this way, the proposal still needs to week"), and so on.

The Fifth Annual Theoretical Issues in Conceptual Information Processing Workshop took place in Washington, D.C. in June 1987. About 100 participants gathered to hear several invited talks and panels discussing the issues relating to artificial intelligence and cognitive science.

Connectionism challenges a basic assumption of much of AI, that mental processes are best viewed as algorithmic symbol manipulations. Connectionism replaces symbol structures with distributed representations in the form of weights between units. For problems close to the architecture of the underlying machines, connectionist and symbolic approaches can make different representational commitments for a task and, thus, can constitute different theories. The connectionist hope of using learning to obviate explicit specification of this content is undermined by the problem of programming appropriate initial connectionist architectures so that they can in fact learn.

Connectionism challenges a basic assumption of much of AI, that mental processes are best viewed as algorithmic symbol manipulations. Connectionism replaces symbol structures with distributed representations in the form of weights between units. For problems close to the architecture of the underlying machines, connectionist and symbolic approaches can make different representational commitments for a task and, thus, can constitute different theories. For complex problems, however, the power of a system comes more from the content of the representations than the medium in which the representations reside. The connectionist hope of using learning to obviate explicit specification of this content is undermined by the problem of programming appropriate initial connectionist architectures so that they can in fact learn. In essence, although connectionism is a useful corrective to the view of mind as a Turing machine, for most of the central issues of intelligence, connectionism is only marginally relevant.

The Fifth Annual Theoretical Issues in Conceptual Information Processing Workshop took place in Washington, D.C. in June 1987. About 100 participants gathered to hear several invited talks and panels discussing the issues relating to artificial intelligence and cognitive science.