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.

The three competitions--(1) AAAI Mobile Robot, (2) AUVS Unmanned Ground Robotics, and (3) IJCAI RoboCup--were used in different years for an introductory undergraduate robotics course, an advanced graduate robotics course, and an undergraduate practicum course. Based on these experiences, a strategy is presented for incorporating competitions into courses in such a way as to foster intellectual maturation as well as learn lessons in organizing courses and fielding teams. The article also provides a classification of the major robot competitions and discusses the relative merits of each for educational projects, including the expected course level of computer science students, equipment needed, and costs. The sponsorship of such competitions ranges from local clubs of enthusiasts to large professional organizations, such as the American Association for Artificial Intelligence (AAAI), which sponsors the annual AAAI Mobile Robot Competition and Exhibition as part of its annual ...

The system consists of an overall agent architecture and five components within the architecture. The five components are (1) goaldirected learning (GDL), a decisiontheoretic method for selecting learning goals; (2) probabilistic bias evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals; (3) uniquely predictive theories (UPTs) and probability computation using independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories; (4) a probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories; and (5) a decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA's initial learning goal is just An autonomous agent must be able to select biases (Mitchell 1980) for new learning tasks as they arise. PBE uses probabilistic background knowledge and a model of the system's expected learning performance to compute the expected value of learning biases for each learning goal. The resulting expected discounted future accuracy is used as the expected value of the bias.

We argue that qualitative modeling provides a valuable way for students to learn. Learning to formulate, test, and revise models is a crucial aspect of understanding science and is critical to helping students become active, lifelong learners. Supporting students in articulating models of a domain and refining them through experience, reflection, and discussion with peers and teachers can lead to deeper, systematic understanding of science (for example, Reif and Larkin [1991]; Collins [1996]). However, modeling formalisms have traditionally been associated with creating mathematical models and deriving numeric results. Such approaches fail to capture many crucial aspects of models, such as the conditions under which a model is applicable, and are relatively inaccessible to younger children, such as middle school students.

Now we'll put physical links and joints together and consider the geometry and the physics required to understand their coordinated motion. We'll learn about the geometry of degrees of freedom. We'll then go back to Newton and learn a compact way to write down the physical dynamics that describes the positions, velocities and accelerations of those degrees of freedom when forced by our actuators.Of course there are many different ways to put limbs and bodies together: again, the animals can teach us a lot as we consider the best morphology for our limbed robots. Sprawled posture runners like cockroaches have six legs which typically move in a stereotyped pattern which we will consider as a model for a hexapedal machine. Nature's quadrupeds have their own varied gait patterns which we will match up to various four-legged robot designs as well.

The Spring Symposium on Knowledge-based Environments for Teaching and Learning focused on the use of technology to facilitate learning, training, teaching, counseling, coaxing and coaching. Sixty participants from academia and industry assessed progress made to date and speculated on new tools for building second generation systems. Selection of topics and participants was motivated by a desire for ideological breadth and depth. Panel leaders included William J. Clancey and Alan Lesgold (researchers of realworld systems); Kurt VanLehn (champion of cognitive models); Beverly Park Woolf (defender of discourse systems); Elliot Soloway (advocate for alternative environments); and Sarah Douglas (spokesperson for supportive systems). Researchers have moved away from building omniscient tutors capable of detecting all possible errors and misconceptions.

Leibniz's (1984) An Introduction to a Secret Encyclopedia includes the following marginal note: Principle of Physical Certainty: Everything which men have experienced always and in many ways will still happen: for example that iron sinks in water (Leibniz 1984). In our daily lives, we routinely use this principle. Thus, we know that we can pull with a string but not push with it; that a flower pot dropped from our balcony falls to the ground and breaks; that when we place a container of water on fire, water might boil after a while and overflow the container. The origin of such knowledge is a matter of constant debate. It is clear that we learn a great deal about the physical world as we grow up.

This article introduces the challenge of digital preservation in the area of engineering design and manufacturing and presents a methodology to apply knowledge representation and semantic techniques to develop digital engineering archives. This work is part of an ongoing, multiuniversity effort to create cyber infrastructure-based engineering repositories for undergraduates (CIBER-U) to support engineering design education. The technical approach is to use knowledge representation techniques to create formal models of engineering data elements, work flows, and processes. With these techniques formal engineering knowledge and processes can be captured and preserved with some guarantee of long-term interpretability. The article presents examples of how the techniques can be used to encode specific engineering information packages and work flows.

Editor The article "IJCAI Policy on Multiple Publication of Papers," by Alan Bundy in the Spring 1989 issue, misses one of the most important functions of IJCAI (or any other conference for that matter): The opportunity to present research to a wide audience of one's peers in person. No journal article can wholly replace the valuable give-and-take discussions that occur after a paper session. The proposed policy would deny this opportunity to researchers who have successfully published their results in a journal. Should AI researchers delay journal submissions until portions of their papers have been accepted by IJCAI or other major AI conferences? Given the vicissitudes of peer review in a methodologicallydivided field, the proposed policy seems like an excellent way to prevent dissemination of recent results Also, as much as the AI community might regard the IJCAI proceedings as an archival publication, the same thing can hardly be said of university tenure and promotion review committees, which in general prefer refereed journal articles over conference papers of whatever kind.

In his new book Alternate Realities: Mathematical Models of Nature and Man (New York: John Wiley and Sons, 1989, 493 pages, $34.95), John L. Casti gives us an impressive, up-todate look at several areas of mathematics that are being applied to the study of biological and sociological systems. These areas, including cellular automata theory, catastrophe theory, nonlinear dynamics and chaos, game theory, and control theory, are finding use on the frontiers of scientific research. Although these areas and their applications are described in various other sources, both on the level of a scientist and a layperson, I know of no other book that brings them all together to show how they can be used in scientific research. However, this book suffers from being written for mathematical specialists and, therefore, limits the potential readership. An opportunity to educate more scientists in the use of mathematical models is regrettably missed.