C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473-486. Academic Press, London, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....some guidance on the kinds of problems that are likely to bene t from the transformations. 1 Introduction Since 1991 Inductive Logic Programming (ILP) systems have been used to construct predictive models for data drawn from diverse domains. These include the sciences [18, 17, 25] engineering [14, 11], language processing [8, 35] environment monitoring [12] and software analysis [4] Like many other algorithms in the eld of machine learning, ILP algorithms construct hypotheses for data by performing a search through a large space. Such a search typically involves generating and then ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive logic programming. Academic Press, London, 1992.

.... framework provided by Inductive Logic Programming (ILP) It is a measure of the promise offered by this setting that ILP programs have already been applied with some success on non trivial problems in molecular biology [13, 14, 19] stress analysis in engineering [6] electronic circuit diagnosis [9], environmental monitoring [8] software engineering [2] and natural language processing [27] These results were all achieved using problem specific background knowledge encoded as logic programs, and without recourse to other methods of statistical analysis. While in theory, the effect of all ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473--486. Academic Press, London, 1992.

....with obtaining such models with an Inductive Logic Programming (ILP) system. Using domain speci c background knowledge, such systems have constructed models for data in software engineering [3] stress analysis in engineering [7] environmental monitoring [8] electronic circuit diagnosis [10], molecular biology and drug design [12, 13, 15] and natural language processing [37] Viewed as tools, these ILP systems are special purpose products developed for the taxonomist interested in accurate classi cation schemes. It is further understood that the data provided usually called ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473-486. Academic Press, London, 1992.

....explanations is a distinctive feature of Inductive Logic Programming (ILP) systems. Using domain speci c background knowledge, such systems have constructed models for data in software engineering [3] stress analysis in engineering [7] environmental monitoring [8] electronic circuit diagnosis [9], molecular biology and drug design [11, 12, 17] and natural language processing [28] Crucial to these real world applications has been c 2001 Kluwer Academic Publishers. Printed in the Netherlands. newpaper.tex; 6 07 2001; 20:11; p.1 the inclusion of human expertise, that has, in each ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473-486. Academic Press, London, 1992.

....capable of using problemspecific background knowledge to construct automatically clausal definitions that in some sense, generalise a set of instances. This has allowed a novel form of data analysis in molecular biology [15, 16, 25] stress analysis in engineering [8] electronic circuit diagnosis [11], environmental monitoring [10] software 1 engineering [1] and natural language processing [45] Of these, some, such as those described in [1, 10, 11, 25, 45] are naturally classificatory. Others, such as those described in [8, 15, 16] are essentially concerned with predicting values of a ....

.... This has allowed a novel form of data analysis in molecular biology [15, 16, 25] stress analysis in engineering [8] electronic circuit diagnosis [11] environmental monitoring [10] software 1 engineering [1] and natural language processing [45] Of these, some, such as those described in [1, 10, 11, 25, 45], are naturally classificatory. Others, such as those described in [8, 15, 16] are essentially concerned with predicting values of a numerical response variable (for example, chemical activity of a compound) For problems of this latter type, ILP programs have largely been restricted to ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473--486. Academic Press, London, 1992.

....1983] records of their application to non trivial problems date largely from 1991. Since then, ILP systems have been used to construct predictive models for problems in molecular biology and biochemistry [King et al. 1996, King et al. 1992, Muggleton et al. 1992] electronic circuit diagnosis [Feng, 1992] nite element mesh design in engineering [Dolsak and Muggleton, 1992] environmental data monitoring [Dzeroski et al. 1994] discovery of invariants in software [Bratko and M.Grobelnik, 1993] and natural language processing [Cussens, 1997, Zelle and Mooney, 1993] Despite considerable diversity ....

Feng, C. (1992). Inducing temporal fault dignostic rules from a qualitative model. In Muggleton, S., editor, Inductive Logic Programming, pages 473-486. Academic Press, London. 35

....from the rlgg of a set of positive examples. Negative examples were used to stop rlggs becoming over general. 5.2 Application experience Golem was the first ILP system to be applied to a wide variety of real world applications. These included the construction of a satellite fault diagnosis model [8], the design of a qualitative physics model [2] finite element mesh design [6] protein secondary structure prediction [39] and structure activity prediction for drugs [18] In the qualitative physics domain Golem was hampered in requiring a large tabulation of the QSIM simulator. The determinacy ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming. Academic Press, London, 1992.

....acid sequence [7] Golem rules had an accuracy of 80 on an independent test set. This was considerably higher than results of other comparable approaches. Golem has also been used for building rules for nite element mesh analysis [2] and for building temporal fault diagnosis rules for satellites [4]. 4 Conclusion Inductive Logic Programming is developing into a new logic based technology. The eld uni es induction and deduction within a well founded theoretical framework. ILP is likely to continue extending the boundaries of applicability of machine learning techniques in areas which ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S.H. Muggleton, editor, Inductive Logic Programming. Academic Press, London, 1992.

....illustrate, on a smaller scale, an instance of realising the rst of these outcomes within the province of Inductive Logic Programming (ILP) Since 1991, ILP systems have been used to construct predictive models for data drawn from diverse domains. These include software analysis [2] engineering [7, 9], environment monitoring [8] biochemistry [10, 11, 16] and language processing [4, 27] The systems that have been used to construct these models have at a very coarse level been fairly similar, namely: search engines that repeatedly examine sets of candidates (the search space ) to nd good ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473-486. Academic Press, London, 1992.

.... presented in [43] Relevance of the techniques described here to bio chemical research concerned with structure activity relationships has been reported to that readership in [18] 2 ASHWIN SRINIVASAN AND ROSS KING ogy [20, 21, 30] stress analysis in engineering [9] electronic circuit diagnosis [12], environmental monitoring [11] software engineering [2] and natural language processing [51] These results were all achieved using problem specific background knowledge encoded as logic programs, and without recourse to other methods of statistical analysis. While in theory the effect of all ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473--486. Academic Press, London, 1992.

....capable of using domainspecific background knowledge to construct automatically clausal definitions that in some sense, generalise a set of instances. This has allowed a novel form of data analysis in molecular biology [15, 16, 26] stress analysis in engineering [7] electronic circuit diagnosis [11], environmental monitoring [10] software engineering [1] and natural language processing [49] Of these, 2 some, such as those described in [1, 10, 11, 26, 49] are naturally classificatory. Others, such as those described in [7, 15, 16] are essentially concerned with predicting values of a ....

.... This has allowed a novel form of data analysis in molecular biology [15, 16, 26] stress analysis in engineering [7] electronic circuit diagnosis [11] environmental monitoring [10] software engineering [1] and natural language processing [49] Of these, 2 some, such as those described in [1, 10, 11, 26, 49], are naturally classificatory. Others, such as those described in [7, 15, 16] are essentially concerned with predicting values of a numerical response variable (for example, chemical activity of a compound) For problems of this latter type, ILP programs have largely been restricted to ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473--486. Academic Press, London, 1992.

....to construct a concise theory a set of rules in the same language for the observations. Programs developed within this model have yielded informative theories in a number of areas, such as molecular biology [15, 16, 26] stress analysis in engineering [7] electronic circuit diagnosis [10], environmental monitoring [8] software engineering [3] and natural language processing [40] Taking the term data to be synonymous with the observations, much of this initial success has been achieved on relatively small datasets (a few hundred observations) It can be shown (Section 2.3) ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473--486. Academic Press, London, 1992.

....Satellite diagnosis ILP techniques have been applied to problems within the Aerospace industry. In this case a complete and correct set of rules for diagnosing power supply failures was developed by generating examples from a qualitative model of the power sub system of an existing satellite [37]. The resulting rules are thus guaranteed complete and correct for all single faults since all examples of these were generated from the original model. Rules were described using a simple temporal formalism in which each predicate had an associated time variable. 11.1.4. Rheumatology An ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming. Academic Press, London, 1992.

....[4] Bratko and Muggleton showed that simple naive physics systems could be learned within an ILP setting. In this case, a qualitative model of water filled U tube was learned by Golem from 5 positive examples 6 negative examples and background knowledge representing Kuiper s [15] QSIM theory. In [11] Cao Feng used Golem to construct a complete and correct set of diagnostic rules from a qualitative model of the power subsystem of a European Space Agency satellite. Dolsak and Muggleton [8] used Golem to construct design rules for finite element analysis used within CAD packages. Golem has also ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473--494. Academic Press, London, 1992.

....such as MIS [56] Marvin [55] CIGOL [45] ML SMART [3] FOIL [51] Golem [42] ITOU [53] RDT [25] CLINT [9] FOCL [47] SIERES [62] and LINUS [15] are all capable of synthesising logic programs containing recursion. They can also deal with domains containing explicit representation of time [17] and learn grammar rules for natural language processing [61] Learning in the limit results are well established in the ILP literature both for full clausal logic [48] and definite clause logic [1, 9] These results tell one little about the efficiency of learning. In contrast, Valiant s [60] PAC ....

....[26] List number theory. Quick sort, multiply, etc. 42] Meshes. Rules for Finite Element Mesh design [13] Natural language. Grammar acquisition [61] Proteins. Secondary structure prediction [43] Qualitative. Learning qualitative models [4] Satellites. Temporal fault diagnosis. [17]. In the following subsections some approaches to avoiding these restrictions will be sketched. The problems encountered in applications will be explained and some remedies suggested. 4.1 Ground background knowledge Golem and FOIL require all background knowledge to be given extensionally in ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming. Academic Press, London, 1992.

....the advantages of extending the generalisation techniques beyond those of inverse resolution [14] lie in the fact that fewer examples are required to learn recursive clauses. Recursive clauses have not been vital for the success of several real world applications of Inductive Logic Programming [17, 10, 6, 5]. However, they are of central interest within problems involving construction of arbitrary programs. Traditionally this area has involved deductive techniques within the area known as formal methods. According to a recent paper by Hoare [9] Given specification S, the task is to find a program P ....

C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S.H. Muggleton, editor, Inductive Logic Programming. Academic Press, London, 1992.

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C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming, pages 473-486. Academic Press, London, 1992.

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C. Feng. Inducing temporal fault dignostic rules from a qualitative model. In S. Muggleton, editor, Inductive Logic Programming. Academic Press, London, 1992.

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