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. Humans acquire it through a long process of observation and imitation. 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.

CARMA is an advisory system for rangeland grasshopper infestations that demonstrates how AI technology can deliver expert advice to compensate for cutbacks in public services. CARMA uses two knowledge sources for the key task of predicting forage consumption by grasshoppers: (1) cases obtained by asking a group of experts to solve representative hypothetical problems and (2) a numeric model of rangeland ecosystems. These knowledge sources are integrated through the technique of model-based adaptation, in which case-based reasoning is used to find an approximate solution, and the model is used to adapt this approximate solution into a more precise solution. The combination of a simple interface, flexible control strategy, and integration of multiple knowledge sources makes CARMA accessible to inexperienced users and capable of producing advice comparable to that produced by human experts.

CARMA is an advisory system for rangeland grasshopper infestations that demonstrates how AI technology can deliver expert advice to compensate for cutbacks in public services. CARMA uses two knowledge sources for the key task of predicting forage consumption by grasshoppers: (1) cases obtained by asking a group of experts to solve representative hypothetical problems and (2) a numeric model of rangeland ecosystems. These knowledge sources are integrated through the technique of model-based adaptation, in which case-based reasoning is used to find an approximate solution, and the model is used to adapt this approximate solution into a more precise solution. CARMA has been used in Wyoming counties since 1996. The combination of a simple interface, flexible control strategy, and integration of multiple knowledge sources makes CARMA accessible to inexperienced users and capable of producing advice comparable to that produced by human experts. Moreover, because CARMA embodies diverse forms of expertise, it has been used in ways that its developers did not anticipate, including pest management research, development of industry strategies, and in-state and federal pest-management policy decisions.

This article presents an overview and survey of current work in case-based reasoning (CBR) integrations. There has been a recent upsurge in the integration of CBR with other reasoning modalities and computing paradigms, especially rule-based reasoning (RBR) and constraint-satisfaction problem (CSP) solving. CBR integrations with modelbased reasoning (MBR), genetic algorithms, and information retrieval are also discussed. This article characterizes the types of multimodal reasoning integrations where CBR can play a role, identifies the types of roles that CBR components can fulfill, and provides examples of integrated CBR systems.

This article presents an overview and survey of current work in case-based reasoning (CBR) integrations. There has been a recent upsurge in the integration of CBR with other reasoning modalities and computing paradigms, especially rule-based reasoning (RBR) and constraint-satisfaction problem (CSP) solving. CBR integrations with modelbased reasoning (MBR), genetic algorithms, and information retrieval are also discussed. This article characterizes the types of multimodal reasoning integrations where CBR can play a role, identifies the types of roles that CBR components can fulfill, and provides examples of integrated CBR systems. Past progress, current trends, and issues for future research are discussed.

Although today's world offers us unprecedented access to greater and greater amounts of electronic information, we are faced with significant problems when it comes to finding the right information at the right time -- the essence of the information-overload problem. One of the proposed solutions to this problem is to develop technologies for automatically learning about the implicit and explicit preferences of individual users to customize and personalize the search for relevant information. In this article, we describe the development of the personalized television listings system (PTV),1 which tackles the information-overload problem associated with modern TV listings data by providing an Internet-based personalized TV listings service so that each registered user receives a daily TV guide that has been specially compiled to suit his/her particular viewing preferences.

Deployed applications are three-dimensional scenes, speech input Rapid Development of a systems that have been in use for at for information access, multimodal Course-of-Action Critiquer," by Gheorghe least several months by individuals or dialog, machine learning in engineering Tecuci, Mihai Boicu, Mike Bowman, organizations other than their developers, design, ontologies, agent models, and Dorin Marcu, describes a critiquing have measurable benefits, and and case-based reasoning.

The Third International Conference on Case-Based Reasoning was held at the Seeon Monastery, Bavaria, 27 to 30 July 1999. About 120 researchers from 21 countries attended. The conference included 4 workshops; 3 invit-ed talks; 24 technical presentations; a poster session; and an Industry Day, where the focus was on mature technologies and applications in industry.

The Third International Conference on Case-Based Reasoning was held at the Seeon Monastery, Bavaria, 27 to 30 July 1999. About 120 researchers from 21 countries attended. The conference included 4 workshops; 3 invit-ed talks; 24 technical presentations; a poster session; and an Industry Day, where the focus was on mature technologies and applications in industry.

The seventh biennial Bar-Ilan International Symposium on the Foundations of Artificial Intelligence, will be held on June 25-27, 2001 in Ramat Gan, Israel. The meeting will honor the research and accomplishments of Yaacov Choueka and will therefore place special emphasis on natural language processing and computational linguistics, in addition to the usual topics of the symposium. Yaacov Choueka Jieh Hsiang Daphne Koller Richard Korf Doug Lenat Moshe Vardi The BISFAI-01 program, schedule and registration information will be available at the BISFAI website: www.cs.biu.ac.il/ bisfai, along with abstracts of invited and accepted papers and pointers to online versions.For further information or requests, contact: bisfai@cs.biu.ac.il. CONTEXT-01 EST Setubal, Campus do IPS / R. Vale www.dfki.de/um2001 Faculty Positions for Intelligent Aerospace Systems Program The College of Engineering at the University of Oklahoma invites applications for 3 to 5 new faculty positions at all levels in the area of Intelligent Systems.