Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Causal model progressions as a foundation for intelligent learning environments
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Model-based reasoning about learner behaviour
Artificial Intelligence
Modeling Biological Systems: Principles and Applications
Modeling Biological Systems: Principles and Applications
Beyond Interaction Design: Beyond Human-Computer Interaction
Beyond Interaction Design: Beyond Human-Computer Interaction
Qualitative simulation and related approaches for the analysis of dynamic systems
The Knowledge Engineering Review
The Ants' Garden: Complex interactions between populations and the scalability of qualitative models
AI Communications - Binding Environmental Sciences and AI
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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Ecological knowledge is often characterised as being incomplete, sparse and non-formalised. Qualitative reasoning provides means to capture such knowledge that is otherwise difficult to represent in computer programs. An additional feature is that qualitative models can be used to run interactive simulations in learning environments, providing opportunities for learners to acquire causal insights about ecological phenomena. In this paper we present qualitative models of interactions between two populations in biological communities. Our approach further explores a qualitative theory of population dynamics previously implemented. Based on this theory we have developed and implemented qualitative models and simulations that support reasoning about the most common behaviours of two interacting populations. In our models the assumptions are explicitly represented and therefore can be analysed by students and modellers. We also discuss how these models can be organised to create interesting learning routes for teaching learners about population and community behaviour.