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Fattah, Yousri El; O'Rorke, Paul

doi:10.1023/a:1022631512320

Cited by 3 publications

References 22 publications

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Bounding the cost of learned rules

Kim

Rosenbloom

2000

Artificial Intelligence

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In this article we approach one key aspect of the utility problem in explanationbased learning EBL | the expensive-rule problem | a s a n a voidable defect in the learning procedure. In particular, we examine the relationship between the cost of solving a problem without learning versus the cost of using a learned rule to provide the same solution, and refer to a learned rule as expensive if its use is more costly than the original problem solving from which i t w as learned. The key idea we explore is that expensiveness is inadvertently and unnecessarily introduced into learned rules by the learning algorithms themselves. This becomes a particularly powerful idea when combined with an analysis tool which identi es these hidden sources of expensiveness, and modi cations of the learning algorithms which eliminate them. The result is learning algorithms for which the cost of learned rules is bounded by the cost of the problem solving that they replace. We i n vestigate this idea through an analysis of EBL Soar , an implementation of explanation-based learning within the Soar architecture. A transformational analysis is used to identify where EBL Soar inadvertently introduces substantial additional costs in the process of converting a problem solving episode into a learned rule | excessive costs which all ultimately turn out to stem from losses of information during learning. Based on these results, a modi ed EBL Soar algorithm | Bounded EBL Soar BEBL Soar | is developed from which all sources of expensiveness have been eliminated. The cost of using a rule learned by BEBL Soar is provably bounded by the cost of the problem solving it replaces.

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Bounding the cost of learned rules

Kim

Rosenbloom

2000

Artificial Intelligence

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A comparison of model-based reasoning and learning approaches to power transmission fault diagnosis

Rayudu

Samarasinghe

Kulasiri

Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems

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An application of model-based reasoning and modelbased learning to an operative diagnostic domain such as electrical power transmission networks is presented. Most of the research in model-based diagnosis is based on maintenance diagnosis. Operative diagnosis, on the other hand, is done while the system is still in operation even afer the fault. We plan to develop an efficient algorithm for operative diagnosis which can handle large domain of faults and multiple faults in real time. In our search toward a better algorithm, we develop and compare two different reasoning methods: diagnosis based on model based reasoning, and diagnosis based on heuristic rules leamt ji-om model based reasoning. This paper presents the results of the comparison.

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A co-operative hybrid algorithm for fault diagnosis in power transmission

Rayudu

Samarasinghe²,

Maharaj³

2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077)

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This paper presents our co-operative hybrid algorithm for fault diagnosis in power transmission networks. When a fault occurs in a transmission network, it must be identified and eliminated as soon as possible. Since control centers are flooded with hundreds of alarm messages during a fault. fault diagnosis, which involves the analysis of alarrn messages, is a time consuming task. Towards the development of a. fault diagnostician, model-based, heuristic, and neural networks are applied to the domain and the results are presented in this paper. The algorithm is a hierarchical model which combines several reasoning methods such as heuristic, temporal and model-based diagnosis and incorporates a network of neural networks at one of the levels of the hierarchy. The working of this co-operative algorithm is discussed and its results are analysed.

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Cited by 3 publications

References 22 publications

Bounding the cost of learned rules

Bounding the cost of learned rules

A comparison of model-based reasoning and learning approaches to power transmission fault diagnosis

A co-operative hybrid algorithm for fault diagnosis in power transmission

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