**Jeroen Keppens and Qiang Shen
School of Informatics, The University of Edinburgh
Appleton Tower, Crichton Street, Edinburgh EH8 9LE, UK
email: {jeroen,qiangs}@inf.ed.ac.uk
**

The predominant knowledge-based approach to automated model
construction, compositional modelling, employs a set of models of
particular functional components. Its inference mechanism takes a
scenario describing the constituent interacting components of a
system and translates it into a useful mathematical model. This
paper presents a novel compositional modelling approach aimed at
building model repositories. It furthers the field in two respects.
Firstly, it expands the application domain of compositional
modelling to systems that can not be easily described in terms of
interacting functional components, such as ecological systems.
Secondly, it enables the incorporation of user preferences into the
model selection process. These features are achieved by casting the
compositional modelling problem as an activity-based dynamic
preference constraint satisfaction problem, where the dynamic
constraints describe the restrictions imposed over the composition
of partial models and the preferences correspond to those of the
user of the automated modeller. In addition, the preference levels
are represented through the use of symbolic values that differ in
orders of magnitude.

- Introduction
- Dynamic Constraint Satisfaction with Order-of-Magnitude
Preferences
- Background: Activity-based dynamic preference constraint satisfaction
- Order-of-magnitude preferences (OMPs)
- Solving aDPCSPs

- Compositional Model Repositories
- Background: assumption based truth maintenance
- Knowledge Representation
- Inference
- Outline analysis of complexity
- Automated modelling and scientific discovery

- A Population Dynamics Example

- Conclusion and Future Work
- Acknowledgements
- Bibliography
- About this document ...