Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Temporal reasoning in artificial intelligence
Exploring artificial intelligence
Survey of expert critiquing systems: practical and theoretical frontiers
Communications of the ACM
Semantic Granularity in Ontology-Driven Geographic Information Systems
Annals of Mathematics and Artificial Intelligence
Sketching for military courses of action diagrams
Proceedings of the 8th international conference on Intelligent user interfaces
Spatio-Temporal Geographic Information Systems: A Causal Perspective
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
Disciple-RKF/COG: agent teaching by subject matter experts
Eighteenth national conference on Artificial intelligence
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
Behavioral Intelligence for Geospatial Agents in Urban Environments
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A qualitative model of physical fields
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
COSIT'05 Proceedings of the 2005 international conference on Spatial Information Theory
Ontologies in amine platform: structures and processes
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
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In this paper we propose an approach to support "what-if" analysis in the context of COA evaluation. Our approach consists in using multiagent geosimulation to simulate the execution of COAs in a Virtual Geographic Environment (VGE) which can change during the simulation, and then allowing the user to explore various assumptions and to analyse their outcomes. We identify the requirements to support this approach and we present how we implement them in the MAGS-COA software. We also illustrate our approach on an example and we present future works.