TheGeneral Motors Variation-Reduction Adviser is a knowledge system built on case-based reasoning principles that is currently in use in eighteen General Motors asssembly centers. This article reviews the overall characteristics of the system and then focuses on various AI elements critical to support its deployment to a production system. A key AI enabler is ontology-guided search using domainspecific ontologies.

Since 1994, GE Plastics has employed a case-based reasoning (CBR) tool that determines color formulas that match requested colors. This tool, called FormTool, has saved GE millions of dollars in productivity and material (that is, colorant) costs. The technology developed in FormTool has been used to create an online color-selection tool for our customers called ColorXpress Select. A customer innovation center has been developed around the FormTool software.

Additional initial ontologies include: search was used, queries were expanded to include (4) single part issues--relate to only one more words to search for, and thus, relevant vehicle component, such as a ding in a fender; documents could be found. Since the documents (5) multiple part issues--relate to two or more being searched were in a limited parts, especially misalignments, unsatisfactory domain, there were few problems with multiple gaps, malformations of joints between parts; senses of words introducing problems that (6) data analysis--results of analysis of measurement hurt precision. In our database, case entries are data generated by optical and mechanical similar--the textual fields do not contain long gages; and (7) plant locations--zones descriptions, and the content is limited to and stations organized topologically or functionally.

After rigorous evaluation, 150 papers were accepted for oral presentation, and 79 for poster presentation. The analogical and case based reasoning category features 6 papers; auctions and market-based systems features 5 papers, and automated reasoning ... out over the Ocean, the winter State University), Amy Greenwald features 12 papers. Twenty papers sky is brilliant panoply of (Brown University), Marti Hearst will be published in constraint stars and comets, beckoning to (University of California, Berkeley), satisfaction and satisfiability; game adventurers... who seek to divine Sridhar Mahadevan (University of theory and economic models features its mysteries. Machine his year marks the twenty-fifth for Artificial Intelligence pioneer and visionary Jay M. ("Marty") learning, the category with the and the twentieth National Tenenbaum4 who will speak on largest number of papers, has 35, Conference on AI (AAAI-05).1 The "The Future of AI and the Web"; while machine perception has 6.

Case-based reasoning [23, 1, 32] is a problem solving methodology based on using a library of solutions for similar problems, i.e., a library of "cases" with their respective solutions. Roughly speaking, case-based planning consists into storing generated plans and using them for finding new plans [15, 8, 29]. In practice, what is stored is not only a specific problem with a specific solution, but also some additional information that is considered useful to the aim of solving new problems, e.g., information about how the plan has been derived [30], why it works [20, 16], when it would not work [17], etc. Different case-based planners differ on how they store cases, which cases they retrieve when the solution of a new problem is needed, how they adapt a solution to a new problem, whether they use one or more cases for building a

Ohio State University) focused on the use of case-based reasoning for both planning and world modeling. Nicola Muscettola (NASA Ames) focused on reactive behaviors. Laboratory) described an approach Within this general theme, to planning with multiagent the aim was to bring together researchers execution. The presentation ecent developments in multiagent shown promising results in the robotics, intelligent autonomous of Thomas Wagner (University of modeling of autonomous, collaborative vehicles). The common denominator Brement), Christoph Schlieder (University behavior between agents in different that these groups share is the pragmatic of Bamberg), and Ubbo Visser environments.

Case-based reasoning (CBR) is a computational reasoning paradigm that involves the storage and retrieval of past experiences to solve novel problems. It is an approach that is particularly relevant in scientific domains, where there is a wealth of data but often a lack of theories or general principles. This article describes several CBR systems that have been developed to carry out planning, analysis, and prediction in the domain of molecular biology.

Thus, one of the primary goals of a CBR system is to find the most similar, or most relevant, cases for new input problems. The effectiveness of CBR depends on the quality and quantity of cases in a case base. In some domains, even a small number of cases provide good solutions, but in other domains, an increased number of unique cases improves problemsolving capabilities of CBR systems because there are more experiences to draw on. The reader can find detailed complete theories, and rapid evolution; reasoning descriptions of the CBR process and systems in is often based on experience rather Kolodner (1993). Experts remember are presented in Leake (1996), and practically positive experiences for possible reuse of solutions; negative experiences are used to avoid oriented descriptions of CBR can be potentially unsuccessful outcomes.

The evolution of the World Wide Web from a repository of HTML data to a source of varied distributed services creates exciting opportunities for offering complex, integrated services over the web. The syntactic problems of such integration are being addressed by the advent of the web services stack of standards.1 However, the promise of service integration will not be delivered unless services can be integrated semantically as well. The 2002 AAAI workshop entitled "Intelligent Service Integration" examined this new challenge for the AI community.

In this article, we first survey the three major types of computer music systems based on AI techniques: (1) compositional, (2) improvisational, and (3) performance systems. For this reason, previous approaches, based on following musical rules trying to capture interpretation knowledge, had serious limitations. An alternative approach, much closer to the observation-imitation process observed in humans, is that of directly using the interpretation knowledge implicit in examples extracted from recordings of human performers instead of trying to make explicit such knowledge. In the last part of the article, we report on a performance system, SAXEX, based on this alternative approach, that is capable of generating high-quality expressive solo performances of jazz ballads based on examples of human performers within a case-based reasoning (CBR) system.