The vertical launch system (vls) tech-assist expert system is being used by the in-service engineering agent as a force multiplier to maintain the readiness, with fewer resources, of a growing population of vlss in the U.S. Navy fleet. This article describes the collaborative development of this knowledge-based system for diagnosis; its main features, including case-based and model-based reasoning; and the lessons we learned from the process.

This report stems from an April 1994 meeting, organized by AAAI at the suggestion of Steve Cross and Gio Wiederhold.1 The purpose of the meeting was to assist ARPA in defining an agenda for foundational AI research. Prior to the meeting, the fellows and officers of AAAI, as well as the report committee members, were asked to recommend areas in which major research thrusts could yield significant scientific gain -- with high potential impact on DOD applications -- over the next ten years. At the meeting, these suggestions and their relevance to current national needs and challenges in computing were discussed and debated. An initial draft of this report was circulated to the fellows and officers. The final report has benefited greatly from their comments and from textual revisions contributed by Joseph Halpern, Fernando Pereira, and Dana Nau.

The Association for the Advancement of Artificial Intelligence sponsored a number of workshops in conjunction with the Eleventh National Conference on Artificial Intelligence held 11-15 July 1993 in Washington, D.C. This article contains reports of four of the workshops that were conducted: AI Models for System Engineering, Case-Based Reasoning, Reasoning about Function, and Validation and Verification of Knowledge Based Systems.

The Association for the Advancement of Artificial Intelligence sponsored a number of workshops in conjunction with the Eleventh National Conference on Artificial Intelligence held 11-15 July 1993 in Washington, D.C. This article contains reports of four of the workshops that were conducted: AI Models for System Engineering, Case-Based Reasoning, Reasoning about Function, and Validation and Verification of Knowledge Based Systems.

In his model, requirements needs, it must be able to represent is done unintentionally; a problem for filling system knowledge solver attempting to solve a gaps also direct explanation generation what these needs are. Ram proposed problem simply stores a trace of its by guiding retrieval and revision representations that include processing without attention to its of explanations during case-based the desired knowledge (possibly partially future relevance. However, Ng's previously explanation construction (Leake specified) and the reason that mentioned studies show that 1992). In the context of analogical the knowledge is sought. Leake for a different class of task, learning mapping, Thagard pointed out that focused on the representation of the goals have a strong effect on the goals, semantic constraints, and syntactic knowledge required to resolve anomalies learning performance of human constraints all affect analogical (which depends on a vocabulary learners. A future question is to identify mapping (Holyoak and Thagard 1989) of anomaly characterization structures the limits of goal-driven processing and the retrieval of potential analogs to describe the information in human learners.

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.

Remember, these are only the winners. It is reducing customers' software (KBS) vendor were touted as a natural fit for AI I think It is interesting to note that other $200,000 in personnel costs; other not. I believe it is more a sign of the AI techniques, beyond traditional benefits include increased product (downsizing) times and the need for representation and reasoning, are sales from higher customer satisfaction increased visibility for the conference. In I saw many good signs at the conference systems. In particular are multiple addition, AT&T reports increases in that applied AI is alive and uses of fuzzy logic, case-based reasoning, the quality of work produced and job healthy.

This article describes Compaq QUICKSOURCE, an electronic problem-solving and information system for Compaq's line of networked printers. A major goal in designing this system was to empower Compaq's customers with expert system technology, allowing them to solve advanced network printer problems entirely on their own. This process minimizes customer down time; reduces the number of telephone calls to the Compaq Customer-Support Center (resulting in monetary savings); improves customer satisfaction; and, perhaps most importantly, differentiates Compaq printers in the market-place by providing the best and most technologically advanced customer-support facility. This approach also represents a reengineering of Compaq's customer-support strategy and implementation. In its first-generation system, SMART, the objective was to provide expert knowledge to Compaq's help-desk operation to better and more quickly answer customer calls and problems. QUICKSOURCE is a second-generation system in that the customer-support function is put directly in the hands of the consumers (an example of knowledge publishing). As a result, its design presented a number of different and challenging issues. Because the product would be used by a diverse and heterogeneous set of users, a significant amount of human factors research and analysis was performed as part of system design and implementation. The analysis also dictated certain decisions about the organization and design of the expert system component. Since September 1992, Compaq has shipped more than 3000 copies of QUICKSOURCE.

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.

Expertise consists of rapid selection and application of compiled experience. Robust reasoning, however, requires adaptation to new contingencies and intelligent modification of past experience. This article presents a comprehensive computational model of analogical (case-based) reasoning that transitions smoothly between case replay, case adaptation, and general problem solving, exploiting and modifying past experience when available and resorting to general problem-solving methods when required. Learning occurs by accumulation of new cases, especially in situations that required extensive problem solving, and by tuning the indexing structure of the memory model to retrieve progressively more appropriate cases. The derivational replay mechanism is discussed in some detail, and extensive results of the first full implementation are presented.