On time and space decomposition of complex structures
Communications of the ACM
Artificial Intelligence
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Structured induction in expert systems
Structured induction in expert systems
System identification
KARDIO: a study in deep and qualitative knowledge for expert systems
KARDIO: a study in deep and qualitative knowledge for expert systems
An Automated Approach to Information Systems Decomposition
IEEE Transactions on Software Engineering
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Inductive logic programming: derivations, successes and shortcomings
ACM SIGART Bulletin
Qualitative system identification: deriving structure from behavior
Artificial Intelligence
Learning Qualitative Models of Dynamic Systems
Machine Learning - special issue on inductive logic programming
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
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
Qualitatively faithful quantitative prediction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A study of applying dimensionality reduction to restrict the size of a hypothesis space
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Qualitative system identification from imperfect data
Journal of Artificial Intelligence Research
The Knowledge Engineering Review
Knowledge-Guided identification of petri net models of large biological systems
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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The use of computational models is increasingly expected to play an important role in predicting the behaviour of biological systems. Models are being sought at different scales of biological organisation namely: sub-cellular, cellular, tissue, organ, organism and ecosystem; with a view of identifying how different components are connected together, how they are controlled and how they behave when functioning as a system. Except for very simple biological processes, system identification from first principles can be extremely difficult. This has brought into focus automated techniques for constructing models using data of system behaviour. Such techniques face three principal issues: (1) The model representation language must be rich enough to capture system behaviour; (2) The system identification technique must be powerful enough to identify substantially complex models; and (3) There may not be sufficient data to obtain both the model's structure and precise estimates of all of its parameters. In this paper, we address these issues in the following ways: (1) Models are represented in an expressive subset of first-order logic. Specifically, they are expressed as logic programs; (2) System identification is done using techniques developed in Inductive Logic Programming (ILP). This allows the identification of first-order logic models from data. Specifically, we employ an incremental approach in which increasingly complex models are constructed from simpler ones using snapshots of system behaviour; and (3) We restrict ourselves to "qualitative" models. These are non-parametric: thus, usually less data are required than for identifying parametric quantitative models. A further advantage is that the data need not be precise numerical observations (instead, they are abstractions like positive, negative, zero, increasing, decreasing and so on). We describe incremental construction of qualitative models using a simple physical system and demonstrate its application to identification of models at four scales of biological organisation, namely: (a) a predator-prey model at the ecosystem level; (b) a model for the human lung at the organ level; (c) a model for regulation of glucose by insulin in the human body at the extra-cellular level; and (d) a model for the glycolysis metabolic pathway at the cellular level.