Agent-Based Modelling for Ecology

Agent-Based Modelling for Ecology

Understanding the complexity of past vegetation in space and through time requires a level of detail which begins at the level of the individual plant with its internal processes, such as tolerance to environmental factors, competition from other plants, reproduction, and dissemination of progenies. Whilst this may appear to be entirely unattainable using established methodologies, it can be approached through Agent-Based Modelling (ABM) or Individual-Based Modelling (IBM), also called Individual-Based Ecology (IBE), which has been described by Huston et al. (1988) as an approach with the potential to unify ecological theory. ‘Modelling agents’ implies determining the properties and functions associated with an individual entity (such as a plant) and how that entity interacts with both other entities and its environment. The research uses a ‘bottom-up’ approach founded on the principles of Complexity.


  • Ch’ng E. (2012) Macro and Micro Environment for Diversity of Behaviour in Artificial Life Simulation, Artificial Life Session, The 6th International Conference on Soft Computing and Intelligent Systems, The 13th International Symposium on Advanced Intelligent Systems, 20-24 November 2012, Kobe, Japan.
  • E. Ch’ng (2011) Spatially Realistic Positioning of Plants for Virtual Environments: Simple Biotic and Abiotic Interaction for Populating Terrains. IEEE Computer Graphics and Applications 31(4), p66-77, July-Aug. 2011.
  • E. Ch’ng (2009) Ground Cover and Vegetation in Level Editors: Learning from Ecological Modelling, 14th International Conference on Computer Games: AI, Animation, Mobile, Interactive Multimedia, Educational & Serious Games, Louisville, Kentucky, USA.
  • Ch’ng, E. (2009) An Artificial Life-Based Vegetation Modelling Approach for Biodiversity Research, in Nature-Inspired informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science and Engineering, R. Chiong, Editor. 2009, IGI Global: Hershey, PA.
  • Ch’ng, E. (2009). “An Efficient Segmentation Algorithm For Entity Interaction.” Biodiversity Informatics, 6(1), p. 5-17. [link to article]
  • Ch’ng, E. (2009). “A Behavioural Agent Model for Synthesizing Vegetation Distribution Patterns on 3D Terrains.” Taylor & Francis, Applied Artificial Intelligence 23(1): 78-102. [manuscript]
  • Ch’ng, E. (2007). Modelling the Adaptability of Biological Systems. The Open Cybernetics and Systemics Journal 1: 13 – 20.
  • Ch’ng E., and Stone R.J., (2006). 3D Archaeological Reconstruction and Visualization: An Artificial Life Model for Determining Vegetation Dispersal Patterns in Ancient Landscapes. IEEE Computer Society. Computer Graphics, Imaging and Vision, CGiV’06, 25-28 July 2006, Sydney, Australia.
  • Ch’ng E. and Stone R.J., (2006). Enhancing Virtual Reality with Artificial Life: Reconstructing a Flooded European Mesolithic Landscape. Presence: Teleoperators and Virtual Environments, June 2006 Special Issue on Virtual Heritage, Presence 15 (3), pp. 341-352.
  • Ch’ng E., Stone R.J., Arvanitis T.N., (2005). Evaluating Artificial Life-based Vegetation Dynamics in the Context of a Virtual Reality Representation of Ancient Landscapes. Virtual Systems and Multimedia, VSMM2005. Ghent, Belgium Oct 3-6, 2005, pp 112-118.


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