The theory of argumentation is a rich, interdisciplinary area of research straddling the fields of artificial intelligence, philosophy, communication studies, linguistics and psychology. In the last few years, significant progress has been made in understanding the theoretical properties of different argumentation logics. However, one major barrier to the development and practical deployment of argumentation systems is the lack of a shared, agreed notation or ‘interchange format’ for argumentation and arguments. In this paper, we describe a draft specification for an argument interchange format (AIF) intended for representation and exchange of data between various argumentation tools and agent-based applications. It represents a consensus ‘abstract model’ established by researchers across fields of argumentation, artificial intelligence and multi-agent systems. In its current form, this specification is intended as a starting point for further discussion and elaboration by the community, rather than an attempt at a definitive, all-encompassing model. However, to demonstrate proof of concept, a use case scenario is briefly described. Moreover, three concrete realizations or ‘reifications’ of the abstract model are illustrated.

This paper reviews the state of the art in model-based systems and qualitative reasoning, and considers where the field will be in 20 years time. It highlights six areas where developments in model-based systems in general, and in qualitative reasoning in particular, have the potential to provide significant computer-based help. The paper also examines where further technological developments might be needed in order to achieve these qualitative futures.

We were faced with a shelved knowledge system at a health insurance company. The system used Bayesian statistics. For correct computations such a system is of course well suited, but not for the people who use it: people do not easily formulate their expertise in numerical probabilities. We addressed the question of whether it is possible to find out in which format experts do represent their knowledge, and if so, what format that is. This representation might then replace the format of numerical probabilities in the knowledge system's interface, increasing its ease of use. We found one method that includes, apart from a technique to elicit knowledge contents, techniques to also acquire its format: Cognitive Structure Analysis. In this contribution we discuss this method, use it in two studies, and show that it yields valid results. It gives insight into the format of experts' knowledge structures; experts turn out to use mainly frame representations.

Agent software technologies are currently still in an early stage of market development, where, arguably, the majority of users adopting the technology are visionaries who have recognized the long-term potential of agent systems. Some current adopters also see short-term net commercial benefits from the technology, and more potential users will need to perceive such benefits if agent technologies are to become widely used. One way to assist potential adopters to assess the costs and benefits of agent technologies is through the sharing of actual deployment histories of these technologies. Working in collaboration with several companies and organizations in Europe and North America, we have studied deployed applications of agent technologies, and we present these case studies in detail in this paper. We also review the lessons learnt, and the key issues arising from the deployments, to guide decision-making in research, in development and in implementation of agent software technologies.