Methods for qualitative simulation allow predictions on the dynamics of a system to be made in the absence of quantitative information, by inferring the range of possible qualitative behaviors compatible with the structure of the system. This article reviews QSIM and other qualitative simulation methods. It discusses two problems that have seriously compromised the application of these methods to realistic problems in science and engineering: the occurrence of spurious behavior predictions and the combinatorial explosion of the number of behavior predictions. In response to these problems, related approaches for the qualitative analysis of dynamic systems have emerged: qualitative phase-space analysis and semi-quantitative simulation. The article argues for a synthesis of these approaches in order to obtain a computational framework for the qualitative analysis of dynamic systems. This should provide a solid basis for further upscaling and for the development of model-based reasoning applications of a wider scope.

Explanation of reasoning is one of the most important abilities an expert system should provide in order to be widely accepted. In fact, since MYCIN, many expert systems have tried to include some explanation capability. This paper reviews the methods developed to date for explanation in heuristic expert systems.

The realization that knowledge management (KM) is primarily a management science and not a computer science implies a different role for technology in KM. This role is concerned with supporting and extending human interaction and learning, and has therefore the need for intelligence-enhanced, integrated and personalized solutions including agent-based approaches. A people-centered view of KM requires support systems that enable the flexible integration of organizational and individual requirements and objectives. This paper introduces the OperA model for organizations that supports individual initiative and collaboration while prescribing a formal structure for organizational processes. The paper introduces the main aspects of the model and describes a case study where OperA is applied to the development of a knowledge-sharing support system.

Advancements in software agents and Semantic Web service technologies are generally enhancing the landscape of electronic commerce. Semantic Web service technologies promise the standardisation and discoverability of software capabilities for network-enabled organisations. Moreover, with the addition of the intelligence and autonomy of software agents, transactions may be equally automated for consumer-to-consumer, business-to-consumer, and business-to-business collaborations. The 2003 Workshop on Electronic Commerce, Agents, and Semantic Web Services was held in conjunction with the International Conference on Electronic Commerce (ICEC2003). The purpose of this workshop was to bring together researchers and practitioners in the areas of electronic commerce, agents, and Semantic Web services to discuss the state-of-art in each individual area in addition to the synergies among the areas. This paper contains a summary of the workshop presentations and a discussion of next steps for Semantic Web services created in the working sessions concluding the workshop.