IBM Palo Alto Scientific Center, 2530 Page Mill Road, Palo Alto, CA 94303 Abstract A pattern recognition algorithm is described that learns a transition net grammar from positive examples. Two sets of examples-one in English and one in Chinese-are presented. It is hoped that language learning will reduce the knowledge acquisition effort for expert systems and make the natural language interface to database systems more transportable. The algorithm presented makes a step in that direction by providing a robust parser and reducing special interaction for introduction of new words and terms. We are developing a natural language interface to an expert system for message processing.

AAAI: It's Time for Large-Scale Systems The most important challenge facing AI today is enabling components to interact in larger scale systems, where modules built with multiple alternative methodologies can be incorporated into robust applications. The infrastructure--computing power, memory, bandwidth, and connectivity--has evolved dramatically. Important theoretical advances have been made in areas such as machine learning, natural language, knowledge representation, task descriptions, sensing, and action in the world. Once again there is substantial demand for AI applications from customers such as DARPA, with a requirement to solve real problems. We need to find ways making AI components interact in larger scale systems.

Requests for information and suggestions for future news columns can be sent by electronic mail to aimagazine0aaaLorg or by US mail to the AAAI office. Suggestions, comments, and questions on any aspect of the society are welcome. AAAI has recently changed its email address. After our Spring Symposia at Stanford University, we received many requests for information on how to -William J. Clancey etI Claudia Mazzetti contact the speakers for further discussion. Below are the names and a mailing address for the authors of presented papers, organized by symposium topic.

A large body of work exists today in the area of design, including Brown and Chandrasekharan (1989) and Dym and Levitt (1991). Also see the Winter 1990 issue of AI Magazine, which was guest edited by J. S. Gero with Mary Lou Maher. By virtue of the teaching experience that supports this book, however, it is more comprehensive and presents its viewpoint in a systematic, orderly progression. The differences between the approaches by different groups are slight. For example, the propose, critique, and modify approach of Chandrasekaran's group (AI Magazine, Winter 1990) is akin to the prototype creation, refinement, and adaptation idea previously described.

Identifying generic, domain-independent tasks, formalizing task representation, elucidating the role of the task in eliciting domain-specific knowledge, and standardizing the design and development of expert systems then became the major research problems of the field. Knowledge specification, includes the task decomposition and the specification of the domain information types and knowledge bases. The task decomposition can be guided by selecting to reuse some of the previously identified task templates. Finally, during knowledge refinement, the models are validated through simulation on paper or with prototyping, and the knowledge bases are refined. Depending on how familiar the analyst is with the domain, these activities might have to be performed repeatedly, and subsequent activities might provide feedback for corrections or extensions to the products of earlier ones.

Automated Problem Solving Group Jet Propulsion Laboratory 4800 Oak Grove Dr. Pasadena, California 91109 AI research at JPL started in 1972 when design and construction of an experimental "Mars Rover" began. Early in that effort, it was recognized that rover planning capabilities were inadequate. Research in planning was begun in 1975, and work on a succession of AI expert systems of steadily increasing power has continued to the present. Within the group, we have concentrated our efforts on expert systems, although work on vision and robotics has continued in a separate organization, with which we have maintained informal contacts. The thrust of our work has been to build expert systems that can be applied in a real-world environment, and to actually put our systems into such environments, taking a consultative responsibility for meeting user requirements.

The goal of this group is to explore the use of domainspecific knowledge and natural deduction-based reasoning techniques to construct theorem provers that operate in nontrivial mathematical domains. Two new provers, by Larry IIines and Tie-Cheng Wang, are very much like expert systems, since the prover takes its direction by trying to satisfy "higher level" goals, based on knowledge about theorem proving. These are stand-alone provers, not man-machine systems, which are attacking some fairly difficult theorems in mathematics. In addition to this mainline work on mathematical theorem provers, two auxiliary efforts rely heavily on knowledge-based deduction. Michael Starbird is developing a knowledge-based expert system for an area of geometric topology, particularly for three dimensions.

Definition of the Problem Domain Processplamirtg is an activity that is performed routinely by industrial engineers in order to schedule and allocate resources for equipment assembly. The planning activity consists of the following: Given a collection of electrical component parts of a printed circuit board together with its layout diagram, derive the sequence of operations required of a technician (or robot) for the correct assembly of the printed circuit board. The plans that result are called operations sheets. At Hazeltine, process planning is an industrial engineering activity that occurs after design and before assembly. In the initial stage of process planning, the industrial engineer carefully examines a parts list (obtained from the bill of materials file; see figure 1) and a detailed physical layout diagram (see figure 2), that shows the proper placement of the corresponding parts on the printed circuit board.

It is written with great intelligence and insight and can benefit a wide audience from advanced undergraduates to seasoned researchers. It is a book that should be in the permanent collection of every AI aficionado because it is such a rich source of ideas and examples. It is not a full-blown AI text; it does not depend on the reader having any previous knowledge of AI but does assume some basic knowledge of Lisp. I have used this book with great success as a supplement in an introductory graduate AI course, the text in a graduate AI course focusing on techniques, and a resource in my research group. The sheer amount of material is impressive, including symbolic mathematics, logic programming, natural language, expert systems, games, and more.

To be considered exceptional, a textbook must satisfy three basic requirements. First, it must be authoritative, written by one with a broad range of experience in, and knowledge of, a subject. Second, it must effectively communicate to the reader, in the same manner in which a course instructor must be capable of imparting knowledge to students in a classroom. Third, it must stimulate the reader into thinking more deeply about the subject and into viewing it from fresh perspectives. In Artificial Intelligence: A Knowledge-Based Approach (Boyd & Fraser, Boston, 740 pp., $48.95), author Morris W. Firebaugh has succeeded in meeting each of these requirements.