Artificial Intelligence - Special volume on qualitative reasoning about physical systems
COPER: a methodology for learning invariant functional descriptions
Machine learning: a guide to current research
Artificial Intelligence
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
System identification
Data-driven approaches to empirical discovery
Artificial Intelligence
KARDIO: a study in deep and qualitative knowledge for expert systems
KARDIO: a study in deep and qualitative knowledge for expert systems
Qualitative physics using dimensional analysis
Artificial Intelligence
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Metamodelling: for bond graphs and dynamic systems
Metamodelling: for bond graphs and dynamic systems
Qualitative system identification: deriving structure from behavior
Artificial Intelligence
Learning Qualitative Models of Dynamic Systems
Machine Learning - special issue on inductive logic programming
Practical Guide to Computer Methods for Engineers
Practical Guide to Computer Methods for Engineers
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: From Machine Learning to Software Engineering
Explanation-Based Generalization: A Unifying View
Machine Learning
An Integrated Framework for Empirical Discovery
Machine Learning
Declarative Bias in Equation Discovery
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Discovering the Structure of Partial Differential Equations from Example Behaviour
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Induction in first order logic from noisy training examples and fixed example set sizes
Induction in first order logic from noisy training examples and fixed example set sizes
On the specification of multiple models for diagnosis of dynamic systems
AI Communications
Toward Verified Biological Models
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The Journal of Machine Learning Research
Incremental state based diagnosis
Advanced Engineering Informatics
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
The Knowledge Engineering Review
An immune-inspired approach to qualitative system identification of biological pathways
Natural Computing: an international journal
Learning qualitative models from numerical data
Artificial Intelligence
Learning qualitative models from numerical data: extended abstract
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the form of qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the precise quantitative model, but also assist in understanding the essence of that model. Our position in this paper is that background knowledge incorporating system modelling principles can be used to constrain effectively the set of good qualitative models. Utilising the model-identification framework provided by Inductive Logic Programming (ILP) we present empirical support for this position using a series of increasingly complex artificial datasets. The results are obtained with qualitative and quantitative data subject to varying amounts of noise and different degrees of sparsity. The results also point to the presence of a set of qualitative states, which we term kernel subsets, that may be necessary for a qualitative model-learner to learn correct models. We demonstrate scalability of the method to biological system modelling by identification of the glycolysis metabolic pathway from data.