In this editorial introduction to this special issue of AI Magazine on qualitative reasoning, we briefly discuss the main motivations and characteristics of this branch of AI research. We also summarize the contributions in this issue and point out challenges for future research.

We argue that qualitative modeling provides a valuable way for students to learn. Two modelbuilding environments, VMODEL and HOMER/- VISIGARP, are presented that support learners by constructing conceptual models of systems and their behavior using qualitative formalisms. Both environments use diagrammatic representations to facilitate knowledge articulation. Preliminary evaluations in educational settings provide support for the hypothesis that qualitative modeling tools can be valuable aids for learning.

We examine different formalisms for modeling qualitatively physical systems and their associated inferential processes that allow us to derive qualitative predictions from the models. We highlight the mathematical aspects of these processes along with their potential and limitations.

Traditional approaches to ecological modeling, based on mathematical equations, are hampered by the qualitative nature of ecological knowledge. In this article, we demonstrate that qualitative reasoning provides alternative and productive ways for ecologists to develop, organize, and implement models. We present a qualitative theory of population dynamics and use this theory to capture and simulate commonsense theories about population and community ecology. Advantages of this approach include the possibility of deriving relevant conclusions about ecological systems without numeric data; a compositional approach that enables the reusability of models representing partial behavior; the use of a rich vocabulary describing objects, situations, relations, and mechanisms of change; and the capability to provide causal interpretations of system behavior.

In general, modeling is a complex and creative task, and building qualitative models is no exception. In this article, we review approaches to learning qualitative models, either from numeric data or qualitative observations. We illustrate this using applications associated with systems control, in particular, the identification and optimization of controllers and human operator's control skill. We also review approaches that learn models in terms of qualitative differential equations.

For the last two decades, configuration systems relying on AI techniques have successfully been applied in industrial environments. These systems support the configuration of complex products and services in shorter time with fewer errors and, therefore, reduce the costs of a mass-customization business model. The European Union-funded project entitled CUSTOMER-ADAPTIVE WEB INTERFACE FOR THE CONFIGURATION OF PRODUCTS AND SERVICES WITH MULTIPLE SUPPLIERS (CAWICOMS) aims at the next generation of web-based configuration applications that cope with two challenges of today's open, networked economy: (1) the support for heterogeneous user groups in an open-market environment and (2) the integration of configurable subproducts provided by specialized suppliers. This article describes the CAWICOMS WORKBENCH for the development of configuration services, offering personalized user interaction as well as distributed configuration of products and services in a supply chain.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2003 Spring Symposium Series, Monday through Wednesday, 24-26 March 2003, at Stanford University. The titles of the eight symposia were Agent-Mediated Knowledge Management, Computational Synthesis: From Basic Building Blocks to High- Level Functions, Foundations and Applications of Spatiotemporal Reasoning (FASTR), Human Interaction with Autonomous Systems in Complex Environments, Intelligent Multimedia Knowledge Management, Logical Formalization of Commonsense Reasoning, Natural Language Generation in Spoken and Written Dialogue, and New Directions in Question-Answering Motivation.

In this article, we discuss the general problems related to the semantic interpretation of WORDNET taxonomy in light of rigorous ontological principles inspired by the philosophical tradition. Then we introduce the DOLCE upper-level ontology, which is inspired by such principles but with a clear orientation toward language and cognition. We report the results of an experimental effort to align WORDNET's upper level with DOLCE. We suggest that such alignment could lead to an "ontologically sweetened" WORDNET, meant to be conceptually more rigorous, cognitively transparent, and efficiently exploitable in several applications.

The PROCESS SPECIFICATION language (PSL) has been designed to facilitate correct and complete exchange of process information among manufacturing systems, such as scheduling, process modeling, process planning, production planning, simulation, project management, work flow, and business-process reengineering. We give an overview of the theories within the PSL ontology, discuss some of the design principles for the ontology, and finish with examples of process specifications that are based on the ontology.

This article presents the methodology that has been successfully used over the past seven years by an interdisciplinary team to create the International Committee for Documentation of the International Council of Museums (CIDOC) CONCEPTUAL REFERENCE MODEL (CRM), a high-level ontology to enable information integration for cultural heritage data and their correlation with library and archive information. The CIDOC CRM is now in the process to become an International Organization for Standardization (ISO) standard. The CIDOC CRM analyzes the common conceptualizations behind data and metadata structures to support data transformation, mediation, and merging. It is assumed that the presented methodology and the upper level of the ontology are applicable in a far wider domain.