Our research is concerned with automated generalisation of topographic vector databases in order to produce maps. This article presents a new, agent-based generalisation model called CartACom (Cartographic generalisation with Communicating Agents), dedicated to the treatment of areas of low density but where rubber sheeting techniques are not sufficient because some eliminations or aggregations are needed. In CartACom, the objects of the initial database are modelled as agents, that is, autonomous entities, that choose and apply generalisation algorithms to themselves in order to increase the satisfaction of their constraints as much as possible. The CartACom model focuses on modelling and treating the relational constraints, defined as constraints that concern a relation between two objects. In order to detect and assess their relational constraints, the CartACom agents are able to perceive their spatial surroundings. Moreover, to make the good generalisation decisions to satisfy their relational constraints, they are able to communicate with their neighbours using predefined dialogue protocols. Finally, a hook to another agent-based generalisation model – AGENT – is provided, so that the CartACom agents can handle not only their relational constraints but also their internal constraints. The CartACom model has been applied to the generalisation of low-density, heterogeneous areas like rural areas, where the space is not hierarchically organised. Examples of results obtained on real data show that it is well adapted for this application.

Cartographic generalisation seeks to summarise geographical information to produce legible maps at smaller scales. Past research led to the development of many automated cartographic generalisation processes, each one being more or less specialised to a particular problem: a landscape like urban areas, a data theme like land use, a cartographic conflict like linear symbol overlap or most of the time of mix of the three. This paper deals with the development of a model allowing collaborative generalisation i.e. the collaboration between automatic processes like these in order to tackle the generalisation of a complete map. CollaGen, our proposed model, allows to partition data in geographic spaces and to find to best suited process to generalise each space. The applications of a process on a space are automatically orchestrated. Interoperability between processes is managed thanks to formal constraints and side effects are monitored after each process application. Results from CollaGen prototype are shown and discussed.

This paper presents an overview of research studies made at the COGIT laboratory of IGN France in the fields of generalisation and symbol specification, particularly considering evaluation aspects. It then discusses how generalisation and symbol specification interact. Finally it explores some possible adaptations of the presented works in generalisation and symbol specification to cartography in the context of crisis management.

Humans frequently have to face complex problems. A classical approach to solve them is to search the solution by means of a trial and error method. This approach is often used with success by artificial systems. However, when facing highly complex problems, it becomes necessary to introduce control knowledge (heuristics) in order to limit the number of trials needed to find the optimal solution. Unfortunately, acquiring and maintaining such knowledge can be fastidious. In this paper, we propose an automatic knowledge revision approach for systems based on a trial and error method. Our approach allows to revise the knowledge off-line by means of experiments. It is based on the analysis of solved instances of the considered problem and on the exploration of the knowledge space. Indeed, we formulate the revision problem as a search problem: we search the knowledge set that maximises the performances of the system on a sample of problem instances. Our knowledge revision approach has been implemented for a real-world industrial application: automated cartographic generalisation, a complex task of the cartography domain. In this implementation, we demonstrate that our approach improves the quality of the knowledge and thus the performance of the system.