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14
Discovering Dynamics
 In Proc. Tenth International Conference on Machine Learning
, 1993
"... Machine discovery is concerned with the task of finding laws from experimental and/or observational data. Existing machine discovery systems have mostly generated laws describing static situations. The paper presents LAGRANGE, a system that constructs a set of differential and/or algebraic equations ..."
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Cited by 27 (6 self)
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Machine discovery is concerned with the task of finding laws from experimental and/or observational data. Existing machine discovery systems have mostly generated laws describing static situations. The paper presents LAGRANGE, a system that constructs a set of differential and/or algebraic equations that describe an observed behavior of a dynamic system. As such, LAGRANGE extends the scope of machine discovery to dynamic systems. We show that LAGRANGE is able to generate appropriate sets of laws for several nonlinear dynamic systems from traces of their behavior. 1 Introduction Consider a simple biological experiment. We place some nutrient and some bacteria in a jar of water. Having done this, we keep the water temperature constant and observe (at regular time intervals) how the concentrations of food and bacteria in the water change over time. Their behavior might look as shown in Figure 1, where c denotes the nutrient concentration and x denotes the bacteria concentration. Typical...
On the use of qualitative reasoning to simulate and identify metabolic pathways
 Bioinformatics
, 2005
"... Perhaps the greatest challenge of modern biology is to develop accurate in silico models of cells. To do this we require computational formalisms for both simulation (how according to the model the state of the cell evolves over time), and identification (learning a model cell from observation of st ..."
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Cited by 18 (4 self)
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Perhaps the greatest challenge of modern biology is to develop accurate in silico models of cells. To do this we require computational formalisms for both simulation (how according to the model the state of the cell evolves over time), and identification (learning a model cell from observation of states). We propose the use of qualitative reasoning (QR) as a unified formalism for both tasks. The two most commonly used alternative methods of modelling biochemical pathways are ordinary differential equations (ODEs), and logical/graphbased (LG) models. Results The QR formalism we use is an abstraction of ODEs. It enables the behaviour of many ODEs, with different functional forms and parameters, to be captured in a single QR model. QR has the advantage over LG models of explicitly including dynamics. To simulate biochemical pathways we have developed “enzyme ” and “metabolite” QR building blocks that fit together to form models. These models are finite, directly executable, easy to interpret, and robust. To identify QR models we have developed heuristic chemoinformatics graph analysis and machine learning procedures. The graph analysis procedure is a series of constraints and heuristics that limit the number of ways metabolites can combine to form pathways. The machine learning procedure is generateandtest inductive logic programming. We illustrate the use of QR for modelling and simulation using the example of glycolysis.
Mining Scientific Data
, 2001
"... The past two decades have seen rapid advances in high performance computing and ..."
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Cited by 14 (4 self)
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The past two decades have seen rapid advances in high performance computing and
Computational Logic and Machine Learning: A roadmap for Inductive Logic Programming
 Technical Report, J. Stefan Institute
, 1998
"... Computational logic has already significantly influenced (symbolic) machine learning through the field of inductive logic programming (ILP) which is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowled ..."
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Cited by 6 (1 self)
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Computational logic has already significantly influenced (symbolic) machine learning through the field of inductive logic programming (ILP) which is concerned with the induction of logic programs from examples and background knowledge. In ILP, the shift of attention from program synthesis to knowledge discovery resulted in advanced techniques that are practically applicable for discovering knowledge in relational databases. Machine learning, and ILP in particular, has the potential to influence computational logic by providing an application area full of industrially significant problems, thus providing a challenge for other techniques in computational logic. This paper gives a brief introduction to ILP, presents stateoftheart ILP techniques for relational knowledge discovery as well as some research and organizational directions for further developments in this area. 1 Introduction Inductive logic programming (ILP) [35, 39, 29] is a research area that has its backgrounds in induct...
Qualitative system identification from imperfect data
, 2002
"... 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 whe ..."
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Cited by 5 (2 self)
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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 modelstructure is rarely known and the modeler has to deal with both modelidentification and parameterestimation. 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
Learning Qualitative Models of Physical and Biological Systems
 IN S. D. DˇZEROSKI & L. TODOROVSKI (EDS.), COMPUTATIONAL
, 2007
"... We present a qualitative modellearning system, Qoph, developed for application to scientific discovery problems. Qoph learns the structural relations between a set of observed variables. It has been shown capable of learning models with intermediate (unmeasured) variables, and intermediate rela ..."
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Cited by 4 (1 self)
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We present a qualitative modellearning system, Qoph, developed for application to scientific discovery problems. Qoph learns the structural relations between a set of observed variables. It has been shown capable of learning models with intermediate (unmeasured) variables, and intermediate relations, under different levels of noise, and from qualitative or quantitative data. A biological application of Qoph is explored. An additional
Machine Learning In Medical Applications
, 1999
"... Research in Machine Learning methods todate remains centered on technological issues and is mostly application driven. This letter summarizes successful applications of machine learning methods that were presented at the Workshop on Machine Learning in Medical Applications. The goals of the works ..."
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Cited by 3 (0 self)
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Research in Machine Learning methods todate remains centered on technological issues and is mostly application driven. This letter summarizes successful applications of machine learning methods that were presented at the Workshop on Machine Learning in Medical Applications. The goals of the workshop were to foster fundamental and applied research in the application of machine learning methods to medical problem solving and to medical research, to provide a forum for reporting significant results, to determine whether Machine Learning methods are able to underpin the research and development on intelligent systems for medical applications, and to identify those areas where increased research is likely to yield advances. A number of recommendations for a research agenda were produced, including both technical and humancentered issues. INTRODUCTION Machine Learning (ML) aims at providing computational methods for accumulating, changing and updating knowledge in intelligent sys...
Learning in Clausal Logic: A Perspective on Inductive Logic Programming
 Computational Logic: Logic Programming and Beyond, volume 2407 of Lecture Notes in Computer Science
, 2002
"... Abstract. Inductive logic programming is a form of machine learning from examples which employs the representation formalism of clausal logic. One of the earliest inductive logic programming systems was Ehud Shapiro’s Model Inference System [90], which could synthesise simple recursive programs like ..."
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Cited by 3 (0 self)
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Abstract. Inductive logic programming is a form of machine learning from examples which employs the representation formalism of clausal logic. One of the earliest inductive logic programming systems was Ehud Shapiro’s Model Inference System [90], which could synthesise simple recursive programs like append/3. Many of the techniques devised by Shapiro, such as topdown search of program clauses by refinement operators, the use of intensional background knowledge, and the capability of inducing recursive clauses, are still in use today. On the other hand, significant advances have been made regarding dealing with noisy data, efficient heuristic and stochastic search methods, the use of logical representations going beyond definite clauses, and restricting the search space by means of declarative bias. The latter is a general term denoting any form of restrictions on the syntactic form of possible hypotheses. These include the use of types, input/output mode declarations, and clause schemata. Recently, some researchers have started using alternatives to Prolog featuring strong typing and real functions, which alleviate the need for some of the above adhoc mechanisms. Others have gone beyond Prolog by investigating learning tasks in which the hypotheses are not definite clause programs, but for instance sets of indefinite clauses or denials, constraint logic programs, or clauses representing association rules. The chapter gives an accessible introduction to the above topics. In addition, it outlines the main current research directions which have been strongly influenced by recent developments in data mining and challenging reallife applications. 1
Qualitatively faithful quantitative prediction
, 2004
"... We describe an approach to machine learning from numerical data that combines both qualitative and numerical learning. This approach is carried out in two stages: (1) induction of a qualitative model from numerical examples of the behaviour of a physical system, and (2) induction of a numerical regr ..."
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Cited by 2 (0 self)
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We describe an approach to machine learning from numerical data that combines both qualitative and numerical learning. This approach is carried out in two stages: (1) induction of a qualitative model from numerical examples of the behaviour of a physical system, and (2) induction of a numerical regression function that both respects the qualitative constraints and fits the training data numerically. We call this approach Q2 learning, which stands for Qualitatively faithful Quantitative learning. Induced numerical models are “qualitatively faithful” in the sense that they respect qualitative trends in the learning data. Advantages of Q2 learning are that the induced qualitative model enables a (possibly causal) explanation of relations among the variables in the modelled system, and that numerical predictions are guaranteed to be qualitatively consistent with the qualitative model which alleviates the interpretation of the predictions. Moreover, as we show experimentally the qualitative model’s guidance of the quantitative modelling process leads to predictions that may be considerably more accurate than those obtained by stateoftheart numerical learning methods. The experiments include an application of Q2 learning to the identification of a car wheel suspension system—a complex, industrially relevant mechanical system.
Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming
"... 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: subcellular, cellular, tissue, organ, organism and ecosystem; with a view of identifyi ..."
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Cited by 1 (1 self)
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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: subcellular, 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 firstorder 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 firstorder logic models from