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Static multimedia on the Web can already be hardly structured manually. Although unavoidable and necessary, manual annotation of dynamic multimedia becomes even less feasible when multimedia quickly changes in complexity, i.e. in volume, modality, and usage context. The latter context could be set by learning or other purposes of the multimedia material. This multimedia dynamics calls for categorisation systems that index, query and retrieve multimedia objects on the fly in a similar way as a human expert would. We present and demonstrate such a supervised dynamic multimedia object categorisation system. Our categorisation system comes about by continuously gauging it to a group of human experts who annotate raw multimedia for a certain domain ontology given a usage context. Thus effectively our system learns the categorisation behaviour of human experts. By inducing supervised multi-modal content and context-dependent potentials our categorisation system associates field strengths of raw dynamic multimedia object categorisations with those human experts would assign. After a sufficient long period of supervised machine learning we arrive at automated robust and discriminative multimedia categorisation. We demonstrate the usefulness and effectiveness of our multimedia categorisation system in retrieving semantically meaningful soccer-video fragments, in particular by taking advantage of multimodal and domain specific information and knowledge supplied by human experts.

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Research on multi-agent systems often involves experiments, also in situations where humans interact with agents. Consequently, the field of experimental (human) sciences becomes more and more relevant. This paper clarifies how things can and often do go wrong in distributed AI experiments. We show the flaws in methodological design in existing papers (both with and without humans) and work out an example involving human test-subjects to introduce the fundamental issues of experimental design. Furthermore, we provide researchers with an approach to improve their experimental design. We wish to stimulate researchers to conduct better experiments -which will benefit us all.

The ARTEMIS CAMMI project aims at developing a joint-cognitive system to optimise human operator's performance under demanding labour conditions. The CAMMI domain applications concern avionics, automotive, and civil emergencies. In this paper we address the development of a jointcognitive system for firefighter commanders to optimise situational and team awareness by reducing the workload through mitigation strategies and an adaptive HMI. A general framework and a research methodology are presented to explore the possibilities of applying the CAMMI building blocks in the development of systems to support the handling of firefighter emergencies.