The vision of future electronic marketplaces (e-markets) is that of markets being populated by autonomous intelligent entities—software, trading, e-agents—representing their users or owners and conducting business on their behalf. For this vision to materialize, one fundamental issue that needs to be addressed is that of trust. First, users need to be able to trust that the agents will do what they say they do. Second, they need to be confident that their privacy is protected and that the security risks involved in entrusting agents to perform transactions on their behalf are minimized. Finally, users need to be assured that any legal issues relating to agents trading electronically are fully covered as they are in traditional trading practices. In this paper we consider the barriers for the adoption of agent technology in electronic commerce (e-commerce) which pertain to trust, security and legal issues. We discuss the perceived risks of the use of agents in e-commerce and the fundamental issue of trust in this context. Issues regarding security, and how some of these can be addressed through the use of cryptography, are described. The impact of the use of agent technology on the users' privacy and how it can be both protected as well as hindered by it is also examined. Finally, we discuss the legal issues that arise in agent-mediated e-commerce and discuss the idea of attributing to software agents the status of legal persons or e-persons and the various implications.

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper.

Building a probabilistic network for a real-life domain of application is a hard and time-consuming process, which is generally performed with the help of domain experts. As the scope and, hence, the size and complexity of networks are increasing, the need for proper management of the elicited domain knowledge becomes apparent. To study the usefulness of ontologies for this purpose, we constructed an ontology for the domain of oesophageal cancer, based on a real-life probabilistic network for the staging of cancer of the oesophagus and the knowledge elicited for its construction. In this paper, we describe the various components of our ontology and outline the benefits of using ontologies in engineering probabilistic networks.

In this paper, we present a survey of the development of the technique of argument diagramming covering not only the fields in which it originated — informal logic, argumentation theory, evidence law and legal reasoning — but also more recent work in applying and developing it in computer science and artificial intelligence (AI). Beginning with a simple example of an everyday argument, we present an analysis of it visualized as an argument diagram constructed using a software tool. In the context of a brief history of the development of diagramming, it is then shown how argument diagrams have been used to analyse and work with argumentation in law, philosophy and AI.