To effectively generate a fast and consistent apartment construction project network, Hyundai Engineering and Construction and Korea Advanced Institute of Science and Technology developed a case- and constraint-based project-planning expert system for an apartment domain. The system, FAS-TRAK- APT, is inspired by the use of previous cases by a human expert project planner for planning a new project and the modification of these cases by the project planner using his/her knowledge of domain constraints. This large-scale, case-based, and mixed-initiative planning system, integrated with intensive constraint-based adaptation, utilizes semantic-level metaconstraints and human decisions for compensating incomplete cases imbedding specific planning knowledge. The case- and constraint-based architecture inherently supports cross-checking cases with constraints during system development and maintenance. This system has drastically reduced the time and effort required for initial project planning, improved the quality and completeness of the generated plans, and is expected to give the company the competitive advantage in contract bids for new contracts.

This article, derived from the 1996 Association for the Advancement of 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 is a product of evolution. Our physical bodies are in many ways overdetermined, unnecessarily complex, and inefficiently designed, that is, the predictable product of the blind search that is evolution. What's manifestly true of our anatomy is also likely true of our cognitive architecture. Natural intelligence is unlikely to be limited by principles of parsimony and is likely to be overdetermined, unnecessarily complex, and inefficiently designed. In this sense, intelligence are many things because is composed of the many elements that have been thrown together over evolutionary timescales. I suggest that in the face of that, searching for minimalism and elegance may be a diversion, for it simply may not be there. Somewhat more crudely put: The human mind is a 400,000-year-old legacy application -- and you expected to find structured programming? I end with a number of speculations, suggesting that there are some niches in the design space of intelligences that are currently underexplored. One example is the view that thinking is in part visual, and hence it might prove useful to develop representations and reasoning mechanisms that reason with diagrams (not just about them) and that take seriously their visual nature. I speculate as well that thinking may be a form of reliving, that re-acting out what we have experienced is one powerful way to think about and solve problems in the world. In this view, thinking is not simply the decontextualized manipulation of abstract symbols, powerful though that may be. Instead, some significant part of our thinking may be the reuse or simulation of our experiences in the environment. In keeping with this, I suggest that it may prove useful to marry the concreteness of reasoning in a model with the power that arises from reasoning abstractly.

How much pull versus push is appropriate? An important concern focused on the value proposition for knowledge management systems. Based on the realization benefit at all stages of the knowledge 97), focused on knowledge structure, that it is impractical to force creation and collection process is not the interaction of distinct knowledge such constraints on users, the matter trivial. The workshop had a panel that provided to a particular problem. Motors). of the workshop was to explore Bradley L. Whitehall is a principal Knowledge management involves His which is expected in a new area.

However, all eligible students are Intelligence (AAAI-98) will be Third Annual Genetic Programming encouraged to apply. After the conference, available in late March by writing to Conference (GP-98), July 22-25 an expense report will be required ncai@aaai.org Please note that the deadline Eleventh Annual Conference on scholarships@aaai.org or at 445 Burgess for early registrations is May 27, 1998. Computational Learning Theory Drive, Menlo Park, CA 94025, The conference will be held July (COLT '98), July 24-26 (theory.lcs.mit. All student scholarship recipients Monona Terrace Convention Center, Fifteenth International Conference will be required to participate in the designed by Frank Lloyd Wright, in on Machine Learning (ICML '98), July Student Volunteer Program to support Madison, Wisconsin.

Understanding the mind is one of the great "holy grails" of twentieth-century research. Regardless of training, most people who come in contact with the field of AI are at least partially motivated by the glimmer of hope that they will get a better understanding of the mind. This quest, of course, is a rich and complex one. It is easy to get mired in minutiae along the way, be they the optimization of an algorithm, the details of a mental model, or the intricacies of a logical argument. Thagard's book attempts to call us back to the larger picture and to draw in new devotees -- and, in general, he succeeds.

CHEMREG is a large knowledge-based system used by Air Products and Chemicals, Inc., to support compliance with regulatory requirements for communicating health and safety information in the shipping and handling of chemical products. This article concentrates on one of the knowledge bases in this system: the case-based reasoner. The case-based reasoner addresses the issue of how proper communication of public health and safety information can be ensured while rapid and cost-effective product evaluation is allowed in the absence of actual hazard testing of the product. CHEMREG generates estimates of hazard data for new products from similar products using an existing relational database as a case library. Implementation of the case-based reasoner in rules and objects using a commercial knowledge-based system shell is described. Although some refinements remain, the performance of the case-based reasoner has met its design goals.

New areas such as knowledge management and multimedia also appeared in the emerging applications track.

MetLife processes over 260,000 life insurance applications a year. Underwriting of these applications is labor intensive. Automation is difficult because the applications include many free-form text fields. MetLife's intelligent text analyzer (MITA) uses the information-extraction technique of natural language processing to structure the extensive textual fields on a life insurance application. Knowledge engineering, with the help of underwriters as domain experts, was performed to elicit significant concepts for both medical and occupational textual fields. A corpus of 20,000 life insurance applications provided the syntactical and semantic patterns in which these underwriting concepts occur. These patterns, in conjunction with the concepts, formed the frameworks for information extraction. Extension of the information-extraction work developed by Wendy Lehnert was used to populate these frameworks with classes obtained from the systematized nomenclature of human and veterinary medicine and the Dictionary of Occupational Titles ontologies. These structured frameworks can then be analyzed by conventional knowledge-based systems. MITA is currently processing 20,000 life insurance applications a month. Eighty-nine percent of the textual fields processed by MITA exceed the established confidence-level threshold and are potentially available for further analysis by domain-specific analyzers.

We present a system, CREWS_NS, that is used in the long-term scheduling of drivers and guards for the Dutch Railways. CREWS_NS schedules the work of about 5000 people. CREWS_NS is built on top of CREWS, a scheduling tool for speeding the development of scheduling applications. CREWS heavily relies on the use of AI techniques and has been built as a white-box system, in the sense that the planner can perceive what is going on, can interact with the system by proposing alternatives or querying decisions, and can adapt the behavior of the system to changing circumstances. Scheduling can be done in automatic, semiautomatic, or manual mode. CREWS has mechanisms for dealing with the constant changes that occur in input data, can identify the consequences of the change, and guides the planner in accommodating the changes in the already built schedules (rescheduling).

The format of the conference will include paper presentations, invited speakers, panel discussions, workshops, and planning and scheduling competitions.