In this article, I discuss the emerging field of artificial intelligence and legal reasoning and review the new book by Anne v.d.L. Gardner, An Artificial Intelligence Approach to Legal Reasoning, published by Bradford/MIT Press (1987, 225 pp., $22.50) as the first book in its new series on the subject. Dworkin 1977, 1985) offer insights valuable to AI, their jurisprudential analyses often raise more questions than they answer and their insights, couched in philosophical discourse, are difficult to harness computationally. Much of the knowledge used in legal reasoning is published, codified, and highly indexed. The legal system maintains extremely detailed records of its cases and commentary on them and except for the lowest-level courts, all cases are published and indexed commercially. For example, Shepard's Citations records and updates all forward and backward pointers for cases; that is, for a given case, all the succeeding cases citing it as well as all the cases it cited.

CLAVIER's central purpose is to find the most The use of composite materials, especially in aerospace applications, is on the increase because of their unique weight and strength qualities. Depending on the orientation of the graphite fibers, a part can be extremely flexible in one direction but rigid in another. In addition, a part made from composite material is both lighter and stronger than aluminum. The increased use of graphite parts, as well as the high cost of a spoiled part (as much as $50,000 for a single part), has put greater reliability and efficiency demands on a relatively new and complex manufacturing process. Composite part fabrication requires two major steps: layup and curing.

Hitachi Data Systems (Santa Clara, CA) has added a download microcode enhancement to its Hi-Track expert system. The enhancement will allow Hi-Track to remotely identify and solve potential problems in a customer's storage subsystem, over the telephone. AT&T Network Systems (Oklahoma, OK) has developed System Test History Analysis, an expert system to lower circuit pack repair expenditures and to isolate and resolve intermittent problems prior to shipment to customers. The system reviews the test history on multiple switching module configurations of digital telecommunications systems equipment. The embedded system analyzes the competition's prices, compares them to Alamo's, and then suggests a suitable pricing alternative.

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. Experts remember positive experiences for possible reuse of solutions; negative experiences are used to avoid potentially unsuccessful outcomes. Similar to other scientific domains, problem solving in molecular biology can benefit from systematic knowledge management using techniques from AI. Case-based reasoning (CBR) is particularly applicable to this problem domain because it (1) supports rich and evolvable representation of experiences--problems, solutions, and feedback; (2) provides efficient and flexible ways to retrieve these experiences; and (3) applies analogical reasoning to solve novel problems.

Due to the increasing importance of service offerings as a revenue source and increasing competition among service providers, it is important for companies to optimize both the customer experience as well as the associated cost of providing the service. This article describes a mixed-initiative system that was created to improve customer support for problems customers encountered with their appliances. The mixed-initiative system improved the correctness of the diagnostic process, the speed of the process, and user satisfaction. The tool has been in use since 1999 and has provided more than $50 million in financial benefits by increasing the percentage of questions that could be answered without sending a field service technician to the customers' homes. These systems are rather popular with companies because they save money--the companies' money, that is.

In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both cocitation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a subpart of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the subtopics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced. Each year since 1993 there has been an international or European conference on CBR. Up to 2007, this conference series produced 672 papers in all. In this report we examine the research themes evident in these papers and identify the most active research topics in CBR. At the 2008 conference we presented an analysis of the research themes in CBR, based on an analysis of the cocitation links in the research literature (Greene et al. 2008). That analysis was based on the core set of 672 papers from the CBR conferences with cocitation data coming from a set of 3461 papers that cite these papers (details on how cocitation links are determined are given later in the article). While cocitation analysis has been proven to be very effective at uncovering relational structure in the research literature (White and Griffith 1981), it has the shortcoming that recent papers will have few cocitation links as papers citing pairs of papers in the core set (that is, the source of cocitation links) have not yet appeared. This issue is evident in the plot of citation counts shown in figure 1 and ultimately makes it impossible to recognize the influence of more recent papers.

This article describes the architecture and AI technology behind an XML-based AI framework designed to streamline e-government form processing. The framework performs several crucial assessment and decision support functions, including workflow case assignment, automatic assessment, followup action generation, precedent case retrieval, and learning of current practices. To implement these services, several AI techniques were used, including rule-based processing, schema-based reasoning, AI clustering, case-based reasoning, data mining, and machine learning. The primary objective of using AI for e-government form processing is of course to provide faster and higher quality service as well as ensure that all forms are processed fairly and accurately. With AI, all relevant laws and regulations as well as current practices are guaranteed to be considered and followed.

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. Representative examples of each type are briefly described. Then, we look in more detail at the problem of endowing the resulting performances with the expressiveness that characterizes human-generated music. This is one of the most challenging aspects of computer music that has been addressed just recently. The main problem in modeling expressiveness is to grasp the performer's "touch," that is, the knowledge applied when performing a score.

This article is an editorial introduction to the research discipline of bioinformatics and to the articles in this special issue. In particular, we address the issue of how techniques from AI can be applied to many of the open and complex problems of modern-day molecular biology. Undoubtedly, bioinformatics is a truly interdisciplinary field: Although some researchers continuously affect wet labs in life science through collaborations or provision of tools, others are rooted in the theory departments of exact sciences (physics, chemistry, or engineering) or computer sciences. This wide variety creates many different perspectives and terminologies. One result of this Babel of languages is that there is no single definition for what the subject of this young field really is.

The American Association for 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. 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 AI Models for System Engineering Workshop included 11 presentations divided into 2 broad categories: (1) the need for using AI in system engineering and (2) existing AI applications in system engineering. A morning discussion centered on large system engineering problems that could benefit from AI: modelbased system engineering, the monitoring of the effects of change in large systems, organizational aspects of system engineering, and the integration of software into large systems.