The Workshop on Future Directions in NLP was held at Bolt Beranek and Newman, Inc. (BBN), in Cambridge, Massachusetts, from 29 November to 1 December 1989. The workshop was organized and hosted by Madeleine Bates and Ralph Weischedel of the BBN Speech and Natural Language Department and sponsored by BBN's Science Development Program.
This article reviews research in the development of plan generation systems. Our goal is to familiarize the reader with some of the important problems that have arisen in the design of planning systems and to discuss some of the many solutions that have been developed in the over 30 years of research in this area. In this article, we broadly cover the major ideas in the field of AI planning and show the direction in which some current research is going. We define some of the terms commonly used in the planning literature, describe some of the basic issues coming from the design of planning systems, and survey results in the area. Because such tasks are virtually never ending, and thus, any finite document must be incomplete, we provide references to connect each idea to the appropriate literature and allow readers access to the work most relevant to their own research or applications.
This article examines the near-term impact of expert system technology on work and the organization. First, an approach is taken for forecasting the likely extent of the diffusion, or success, of the technology. Next, the case of advanced manufacturing technologies and their effects is considered. From this analysis, a framework is constructed for viewing the impact of these technologies -- and technologies in general -- as a function of the technology itself; market realities; and personal, organizational, and societal values and policy choices. Two scenarios are proposed with respect to the application of this framework to expert systems. The first concludes that expert systems will have little impact on the nature of work and the organization. The second scenario posits that expert system diffusion will be pulled by, and will be a contributing factor toward, the evolution of the lean, flexible, knowledge-intensive, postindustrial organization.
This article discusses frameworks for studying expertise at the knowledge level and knowledge-use level. It reviews existing approaches such as inference structures, the distinction between deep and surface knowledge, problem-solving methods, and generic tasks. A new synthesis is put forward in the form of a componential framework that stresses modularity and an analysis of the pragmatic constraints on the task. The analysis of a rule from an existing expert system (the Dipmeter Advisor) is used to illustrate the framework.
Varol Akman, in his letter (AI Magazine, Spring 1990) criticizing QSIM, quotes both me and Janowski, accurately I believe, describing various limitations of QSIM. At the risk of being scolded again for "employing universal truths and unarguable facts" in support of my position, I must point out that it is the responsibility of a scientist or engineer to document clearly the known limitations of any method he develops and publishes. In addition to truth in packaging, a clear and unblinking examination of the limitations of one's own work is an invaluable guide to further research.
If classical planners are ever to automatically plan the actions of the smart machines, particularly robots for the automatic assembly of industrial objects, then they will have to know much more about geometry and topology as well as sensing. Consider that the simple act of changing an object's grasp -- the change might be necessitated by the nature of some assembly goal -- involves the interaction of the geometries of the grasping device and the object if the change is to occur without a collision between the device and the object. Of course, one could ask, Could geometric considerations be divorced from the highly developed symbolic-level planning? That is, could we first synthesize a symbolic plan and then plug in the geometry for the execution of the actions? Experience has shown the answer to, unfortunately, be a big no.