AAAI News the edge of technology now, but we'll be at the point where we can Terry Weymouth of The University of Michigan was cochair and ringmaster for the performances. Grosz, Gordon conferences carried on with quiet the robots managed to perform this McKay Professor of Computer excitement in Washington in July, reasonably well. Science at Harvard, is the new President, AAAI's Autonomous Mobile Robot The third event proved the hardest, succeeding Patrick Hayes of the Competitions drew a stream of rousing and none of the robots completed the University of Illinois. The of MIT is President-Elect. The two events drew competitors robots were asked to select four "In the last several years," Grosz and spectators.

We have expanded review criteria for the technical program, effectively increasing the number of ways in which a submitted paper can qualify for acceptance. Most importantly, we have revised the review procedure to encourage acceptance of a larger number and broader range of papers, as discussed below.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1993 Spring Symposium Series on March 23-25 at Stanford University. This article contains summaries of the eight symposia that were conducted: AI and Creativity, AI and NP-Hard Problems, Building Lexicons for Machine Translation, Case-Based Reasoning and Information Retrieval, Foundations of Automatic Planning, Innovative Applications of Massive Parallelism, Reasoning about Mental States, and Training Issues in Incremental Learning. Technical reports of the symposia AI and Creativity, Building Lexicons for Machine Translation, Case-Based Reasoning and Information Retrieval, Foundations of Automatic Planning, Innovative Applications of Massive Parallelism, Reasoning about Mental States, and Training Issues in Incremental Learning are available from AAAI.

In 1991, Ford Motor Company began deployment of CAPE (computer-aided parts estimating system), a highly advanced knowledge-based system designed to generate, evaluate, and cost automotive part manufacturing plans. cape is engineered on an innovative, extensible, declarative process-planning and estimating knowledge representation language, which underpins the cape kernel architecture. Many manufacturing processes have been modeled to date, but eventually every significant process in motor vehicle construction will be included. Significant cost reductions are among the many benefits CAPE brings to Ford. CAPE is a highly significant system for Ford of Europe in terms of the business needs it satisfies and the corporate acceptance of AI applications: First, CAPE represents a major investment, with significant person-years of effort spent on predeployment development alone. Second, CAPE is the first large-scale production expert system to be deployed within Ford of Europe. Third, cost estimating is a critical business function. With a total annual materials budget of several billion dollars, cost control is at the heart of Ford's business. Fourth, reducing the lead time for new model programs provides a key competitive advantage. CAPE reduces estimating response time by 50 percent. Fifth, this system is enormously ambitious. The final system will capture the combined knowledge of estimating experts in all areas of automotive manufacture.

Because of their occasional need to return to shallow points in a search tree, existing backtracking methods can sometimes erase meaningful progress toward solving a search problem. In this paper, we present a method by which backtrack points can be moved deeper in the search space, thereby avoiding this difficulty. The technique developed is a variant of dependency-directed backtracking that uses only polynomial space while still providing useful control information and retaining the completeness guarantees provided by earlier approaches.

Market price systems constitute a well-understood class of mechanisms that under certain conditions provide effective decentralization of decision making with minimal communication overhead. In a market-oriented programming approach to distributed problem solving, we derive the activities and resource allocations for a set of computational agents by computing the competitive equilibrium of an artificial economy. WALRAS provides basic constructs for defining computational market structures, and protocols for deriving their corresponding price equilibria. In a particular realization of this approach for a form of multicommodity flow problem, we see that careful construction of the decision process according to economic principles can lead to efficient distributed resource allocation, and that the behavior of the system can be meaningfully analyzed in economic terms.

Advances in Real-Time Expert System Technologies

The Ninth International Conference on Machine Learning was held in Aberdeen, Scotland, from 1-3 July 1992, with 198 participants in attendance. The conference covered a broad range of topics drawn from the general area of machine learning, including concept-learning algorithms, clustering, speedup learning, formal analysis of learning systems, neural networks, genetic algorithms, and applications of machine learning. This article briefly touches on six selected talks that were of exceptional interest.

This article contains an overview of recent and ongoing projects at Boeing's Huntsville Advanced Computing Group (ACG). In addition, it contains an overview of some of the work being conducted by Boeing's Advanced Civil Space Systems Group. One aspect of ACG's charter is to support the efforts of other groups at Boeing. Thus, AI is not considered a stand-alone field but, instead, is considered an area that can be used to find both long- and short-term solutions for Boeing and its customers.

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.