This article surveys existing practical implementations of both defeasible and argumentation-based reasoning engines and associated literature. We aim to summarize the current state of the art in the research area, show that there are many similiarities and connections between the various implementations and also highlight the differences regarding evaluation goals and strategies. An important goal of this paper is to argue for the need for well-designed empirical evaluations, as well as formal complexity analysis, in order to justify the practical applicability of a reasoning engine. There are indeed many challenges to be faced in developing implementations of argumentation. Not least of these is the inherent computational complexity of the formal models. We cover some of the ways these challenges have been addressed, and provide pointers for future directions in realizing the goal of practical argumentation.

This literature review focuses on user-centered evaluation (UCE) studies of adaptive and adaptable systems. Usability, perceived usefulness and appropriateness of adaptation are the three most commonly assessed variables. Questionnaires appeared to be the most popular method, followed by interviews and data log analysis. The quality of most questionnaires was questionable, and the reporting of interviews and think-aloud protocols was found to be shallow. Furthermore, data logs need triangulation in order to be useful. The reports encountered lack empirical value. The article models the iterative design process for adaptive and adaptable systems, linked to the goals of UCE: supporting decisions, detecting problems and verifying quality. This model summarizes the variables to be assessed at each stage of the process and the relevant methods to assess them.

The paper provides a review of knowledge management (KM) literature with a focus on recent value creation trends of the KM discipline. The review spans a large spectrum of KM research ranging from the ‘soft’ (socio-organizational) to technical dimensions of KM, published in the academic and trade literature. An interpretive stance is adopted so as to provide a holistic understanding and interpretation of organizational KM research and models. Value creation is grounded in the appropriate combination of human network, social capital, intellectual capital, and technology assets, facilitated by a culture of change. It is argued that to be effective organizations need not only to negotiate their migration from a knowledge sharing to a knowledge creation culture, but also to create sustained organizational and societal values. The latter form the foundation of the proposed ‘knowledge value creation’ concept and represent key organizational and societal challenges faced by modern organizations.

This paper describes an investigation that compared Context-based Reasoning (CxBR) and Contextual Graphs (CxG), two well-known context-driven approaches used to represent human intelligence and decision-making. The specific objective of this investigation was to compare and contrast both approaches to increase the readers’ understanding of each approach. We also identify which, if any, excels in a particular area, and to look for potential synergism between them. This comparison is presented according to 10 different criteria, with some indication of which one excels at each particular facet of performance. We focus the comparison on how each would represent human tactical behavior, either in a simulation or in the real world. Conceptually, these two context-driven approaches are not at the same representational level. This could provide an opportunity in the future to combine them synergistically.