Prometheus and ingenias agent methodologies: a complementary approach.

A great number of methodologies to develop multi-agent systems (MAS) have been proposed in the last few years. But a unique methodology cannot be general enough to be useful for everyone without some level of customization. According to our knowledge, existent agent-based surveillance systems have been developed ad-hoc and no methodology has been followed. We are interested in creating tools that allow to model and to generate monitoring environments. This has motivated the selection of Prometheus and INGENIAS methodologies, to take advantage of both approaches in developing agent-based applications. In this paper a collection of equivalences between the concepts used in both methodologies is described extensively

High-Level Information Fusion in Visual Sensor Networks

Information fusion techniques combine data from multiple sensors, along with additional information and knowledge, to obtain better estimates of the observed scenario than could be achieved by the use of single sensors or information sources alone. According to the JDL fusion process model, high-level information fusion is concerned with the computation of a scene representation in terms of abstract entities such as activities and threats, as well as estimating the relationships among these entities. Recent experiences confirm that context knowledge plays a key role in the new-generation high-level fusion systems, especially in those involving complex scenarios that cause the failure of classical statistical techniques –as it happens in visual sensor networks. In this chapter, we study the architectural and functional issues of applying context information to improve high-level fusion procedures, with a particular focus on visual data applications. The use of formal knowledge representations (e.g. ontologies) is a promising advance in this direction, but there are still some unresolved questions that must be more extensively researched.The UC3M Team gratefully acknowledges that this research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02