Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

TL;DR

This paper introduces a novel compositional modelling approach that builds model repositories for complex systems like ecological ones, incorporating user preferences via dynamic constraint satisfaction with order-of-magnitude preferences.

Contribution

It extends compositional modelling to complex, non-functional systems and integrates user preferences using a dynamic constraint satisfaction framework with symbolic preference levels.

Findings

Expanded application domain to ecological systems

Incorporated user preferences into model selection

Utilized symbolic preference levels with orders of magnitude

Abstract

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.