Combining Three Multi-agent Based Generalisation Models: AGENT, CartACom and GAEL

Duchêne, Cécile; Gaffuri, Julien

doi:10.1007/978-3-540-68566-1_16

Cited by 12 publications

(13 citation statements)

References 2 publications

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“…Different models have been developed on how such agents interact (Duchene and Gaffuri 2008). The most interesting model in the context of generating on-demand base maps is the Automatic GEneralisation New Technology (AGENT) model (Ruas 2000) which is able to control objects regarding their topological relations.…”

Section: Agent-based Generalizationmentioning

confidence: 99%

On-demand base maps on the web generalized according to user profiles

Foerster

Stoter

Oosterom

2012

International Journal of Geographical Information Science

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Thematic content in the form of geodata is becoming increasingly available on the web. However, adequate base map information to support appropriate communication of the thematic content is not available in many cases. This base map information might be served through topographic databases available on the web and optimized by means of automated generalization. This article describes an approach based on user profiles, which formally captures the user requirements (preferences) towards the base map and deploys those profiles in a web-based architecture to generate on-demand maps. Those user profiles, which may differ per user, drive the generalization process and produce base maps on demand by taking into account the user requirements as well as the thematic content. The research in this article specifically focuses on the technological environment that generates on-demand base maps incorporating four basic concepts in the user profile: (1) topology awareness between the base map and the thematic content, (2) user-specific base map information at a non-uniform scale to improve the communication of the thematic content, (3) the map specification and (4) inheritance of user profiles. Both existing and new techniques are explored to realize the environment. The approach is evaluated by applying it to a project in the Netherlands dedicated to disseminating physical planning maps through the web.

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Section: Agent-based Generalizationmentioning

confidence: 99%

On-demand base maps on the web generalized according to user profiles

Foerster

Stoter

Oosterom

2012

International Journal of Geographical Information Science

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“…As discussed by Duchêne and Gaffuri (), each model has its own area of application. AGENT is based on hierarchical interactions between single objects (micro agents) and groups of objects (meso agents).…”

Section: The Multi‐agent Modelmentioning

confidence: 99%

Feature‐Driven Generalization of Isobaths on Nautical Charts: A Multi‐Agent System Approach

Guilbert

2015

Transactions in GIS

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A nautical chart provides a schematic view of the seafloor where isobaths (contour lines joining points of same depth) and depth soundings are generalised to highlight undersea features that form navigational hazards and routes. Considering that the process is ultimately driven by features and their significance to navigation, this article proposes a generalisation strategy where isobath generalisation is controlled by undersea features directly. The seafloor is not perceived as a continuous depth field but as a set of discrete features composed by groups of isobaths. In this article, generalisation constraints and operators are defined at feature level and composed of constraints and operators applying to isobaths. In order to automate the process, a multi-agent system is designed where features are autonomous agents evaluating their environment in order to trigger operations. Interactions between agents are described and an example on a bathymetric database excerpt illustrates the feasibility of the approach.

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“…These considerations are quite decisive in the choice of adaptations that have to be made to the existing models. Using a multi-model solution for generalisation was previously explored in [4] where three scenarios are introduced so as to improve multi-agent solution for generalisation. First, a multi-model method for generalisation was proposed, and developed in [24].…”

Section: Multi-model Solutionsmentioning

confidence: 99%

Representation of Interactions in a Multi-Level Multi-Agent Model for Cartography Constraint Solving

Maudet¹,

Touya²,

Duchêne³

et al. 2014

Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection

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International audienceThe objective of cartographic generalisation is to simplify geographic data in order to create legible maps when scale decreases. It often requires to reason at different levels of abstraction (e.g. a building, a city). To automate this process, Multi-Agent approaches have been used since several years, by modelling map objects (e.g. buildings) as autonomous entities trying to solve constraints through appropriate transformations. Yet, those approaches are not able to deal with all situations that appear between cartographic objects in a map. Indeed, though a map intrinsically involves objects that belong to several description, scale or organisation levels, there is no explicit multi-level representation in agent-based cartographic models. Thus we assume that the use of a multi-level multi-agent model would improve the automated generalisation process. Especially, the PADAWAN model is a multi-agent model offering multi-level capabilities which match quite well the requirements for the multi-level organisation of cartographic objects. In this paper, we expose how we use this model on the one hand, to reify multi-level relations between cartographic agents, and on the other hand, to represent the constraints and the actions proposed to solve them, as interactions between the agents

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Combining Three Multi-agent Based Generalisation Models: AGENT, CartACom and GAEL

Cited by 12 publications

References 2 publications

On-demand base maps on the web generalized according to user profiles

On-demand base maps on the web generalized according to user profiles

Feature‐Driven Generalization of Isobaths on Nautical Charts: A Multi‐Agent System Approach

Representation of Interactions in a Multi-Level Multi-Agent Model for Cartography Constraint Solving

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