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Most applications of inductive logic programming focus on prediction or the discovery of new knowledge. We describe a less common application of ILP namely veri cation and validation of knowledge based systems and multi-agent systems. Using inductive logic programming, partial declarative speci cations of the software can be induced from the behavior of the software. These rules can be readily interpreted by the designers or users of the software, and can in turn result in changes to the software. The approach outlined was tested in the domain of multi-agent systems, more in particular the RoboCup domain.

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An important ingredient in agentmediated Electronic Commerce is the presence of intelligent agents that assist Electronic Commerce participants (e.g., individual users, other agents, organisations). These mediating agents are in principle autonomous agents that will interact with their environments (e.g. other agents and webservers) on behalf of the participants who have delegated tasks to them. For mediating agents a (preference) model of these participants is indispensable. In this paper, a generic mediating agent architecture is introduced. Furthermore, we discuss our view of user preference modelling and its need in agentmediated electronic commerce. We survey the state of the art in the field of preference modelling and suggest that preferences of the involved participants can be modelled by learning from their behaviour. In particular, we employ an existing machine learning method called inductive logic programming (ILP). We argue that this method can be used by mediating agents to detect regularities in the behaviour of the involved participants and induce hypotheses about their preferences automatically and dynamically. Finally, we discuss some advantages and disadvantages of using inductive logic programming as a method for preference modelling and compare it with other approaches.

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An important ingredient in agent-mediated Electronic Commerce is the presence of intelligent mediating agents that assist Electronic Commerce participants (e.g., individual users, other agents, organisations

Expert systems for decision support have recently been successfully introduced in road transport management. These systems include knowledge on traffic problem detection and alleviation. The paper describes experiments in automated acquisition of knowledge on traffic problem detection. The task is to detect road sections where a problem has occured (critical sections) from sensor data. It is necessary to use inductive logic programming (ILP) for this purpose as relational background knowledge on the road network is essential. Preliminary results show that ILP can be used to successfully learn to detect traffic problems.