| Summary: | Compositional modelling is one of the most important knowledge-based approaches to automating domain model construction. However, its use has been limited to physical systems due to the specific presumptions made by existing techniques. Based on a critical survey of existing compositional modellers, the strengths and limitations of compositional modelling for its application in the ecological domain are identified and addressed. The thesis presents an approach for effectively building and (re-)using repositories of models of ecological systems, although the underlying methods are domainindependent. It works by translating the compositional modelling problem into a dynamic constraint satisfaction problem (DCSP). This enables the user of the compositional modeller to specify requirements to the model selection process and to find an appropriate model by the use of efficient DCSP solution techniques. In addition to hard dynamic constraints over the modelling choices, the ecologist/ user of the automated modeller may also have a set of preferences over these options. Because ecological models are typically gross abstractions of very complex and yet only partially understood systems, information on which modelling approach is better is limited, and opinions differ between ecologists. As existing preference calculi are not designed for reasoning with such information, a calculus of partially ordered preferences, rooted in order-of-magnitude reasoning, is also devised within this dissertation. The combination of the dynamic constraint satisfaction problem derived from compositional modelling with the preferences provided by the user, forms a novel type of constraint satisfaction problem: a dynamic preference constraint satisfaction problem (DPCSP). In this thesis, four algorithms to solve such DPCSPs are presented and experimental results on their performance discussed. The resulting algorithms to translate a compositional modelling problem into a DCSP, the order-of-magnitude preference calculus and one of the DPCSP solution algorithms constitute an automated compositional modeller. Its suitability for ecological model construction is demonstrated by applications to two sample domains: a set of small population dynamics models and a large model on Mediterranean vegetation growth. The corresponding knowledge bases and how they are used as part of compositional ecological modelling are explained in detail.
|
|---|
