Towell, G., Shavlik, J.: Knowledge-based Artificial Neural Networks. Artificial Intelligence, Vol. 70. (1994)  Home/Search   Document Details and Download   Summary   Related Articles   Check

....learning algorithm, that approximates Plotkin s least general generalization (lgg) Results show that our initial goal, learning logic programs using neural networks, has been achieved. 1 Introduction The Cascade ARTMAP [21] system is a knowledge based neural network (KBNN) 20] like KBANN [22], RAPTURE [11] and C IL2p [6] that has been shown to outperform other purely analytical or inductive systems in the task of propositional theory refinement: a prior incomplete and or partially correct propositional symbolic knowledge about a problem domain is given to a theory refinement system ....

G. Towell and J. Shavlik. Knowledge-Based Artificial Neural Networks. Artificial Intelligence, pp.119-165, vol. 70, 1994.

....survey of the latter. An important point of the present work is the systematic way the di#erent interpretations has been presented in order to permit their comparison. RBFNs are particularly suitable for integrating the symbolic and connectionist paradigms in the line draw by Towell and Shavlik [78] whose recent developments has been surveyed by Cloete and Zurada [22] This symbolic interpretation permits to consider RBFNs as intrinsically Knowledge Based Networks. Moreover, RBFN have also very di#erent interpretations. They are Regularization Networks so there is the possibility of tuning ....

G. Towell and J.W. Shavlik. Knowledge based artificial neural networks. Artficial Intelligence, 70(4):119--166, 1994.

.... been extensive research activity at combining (or integrating) the symbolic and the connectionist approaches for knowledge representation in expert systems [3, 15, 16, 19] Especially, there are a number of efforts combining symbolic rules and neural networks that map rules into neural networks [4, 9, 18]. In addition, connectionist expert systems [6, 7, 17] are a type of integrated systems that represent relationships between concepts, considered as nodes of a neural network. The above approaches give pre eminence to connectionism and use a neural network as a knowledge base. The strong point of ....

Towell, G., Shavlik, J., "Knowledge-Based Artificial Neural Networks", Artificial Intelligence 70, 1994, 119-165.

.... Logic Programming (ILP) techniques, ii) Hybrid (neural and symbolic) Systems; and (iii) Explanation Based Learning (EBL) algorithms (see [31] Among these, hybrid systems seem to be more appropriate as far as dealing with incorrect background knowledge and theory refinement is concerned [10, 46, 45]. Hybrid systems are not normally push button techniques though, as they typically use traditional neural learning algorithms (such as Backpropagation) which require the adaptation of a learning rate via trial and error. On the other hand, explanation based learning algorithms seem to require ....

....learning could be one of these actions [11] An extension of this work would be to investigate the use of other techniques of machine learning for revising requirements specifications. These include extensions of Inductive Logic Programming, Knowledge based Artificial Neural Networks (KBANN) [46] and Explanation based Neural Networks EBNN [44] and their hybrids, e.g. 33] Experiments on a number of real world problems would allow us to perform more detailed technical evaluation of these techniques, and draw general conclusions on when, why, and for which type of requirements ....

G. G. Towell and J. W. Shavlik. Knowledge-based artificial neural networks. Artificial Intelligence, 70(1):119-165, 1994.

....refined. This has the following advantages: 1) the agent is able to perform reasonably well initially because it can utilize the users prior knowledge and 2) users prior knowledge does not have to be correct as it is refined through learning. Information is derived by extracting rules from KBNNs [62]. In order to map large sized web pages into fixed sized NNs, a concept of sliding window is used. This parses each page considering three words at a time, and the html tags like act as window breakers. Using self generated training examples it can act also as a self tuning agent. Rules of the ....

J. Shavlik and G. G. Towell, "Knowledge-based artificial neural networks, " Artificial Intell., vol. 70, no. 1/2, pp. 119--165, 1994.

....series. We now present a method of obtaining rules directly from the time series. We encode these rules into a neural network using the KBANN encoding method. 3.9. 1 Knowledge Based Artificial Neural Networks Methods for encoding a Boolean rule set into a feedforward network have been proposed [66]. Other methods differ only in the way that they combine the input neurons. The initial network is constructed based on the relationship between rules in the rule set. Rule inputs become input neurons, intermediate results become hidden neurons, final results become output neurons, and ....

G. Towell and J. Shavlik, "Knowledge-based artificial neural networks," Artificial Intelligence, vol. 70, no. 1,2, pp. 119--160, 1994.

....it as possible. 1 INTRODUCTION There has been extensive research activity at combining (or integrating) the symbolic and the connectionist approaches for knowledge representation in expert systems [7, 8, 10] Especially, there are a number of efforts combining symbolic rules and neural networks [2, 3, 6, 9]. They give pre eminence to connectionism and use a neural network as a knowledge base. The main objective is to reduce knowledge elicitation from experts to a minimum. In such approaches, connectionism is mainly used as a means for refining an initial background rule base. Integration with ....

G. Towell and J. Shavlik, `Knowledge-Based Artificial Neural Networks', Artificial Intelligence, 70(1-2), 119-165, (1994).

....produce specialized and personalized IR agents. W W IE is a general extractor system, which creates specialized agents that extract pieces of information from documents in the domain of interest. W w builds its agents based on ideas from the theory refinement community within machine learning [28,29,45]. First, the user provided domain knowledge is compiled into knowledge based neural networks [45] Then, this prior knowledge is refined whenever mining examples become available. By using theory refinement, we are able to find an appealing middle ground between nonadaptive agent programming ....

....agents that extract pieces of information from documents in the domain of interest. W w builds its agents based on ideas from the theory refinement community within machine learning [28,29,45] First, the user provided domain knowledge is compiled into knowledge based neural networks [45]. Then, this prior knowledge is refined whenever mining examples become available. By using theory refinement, we are able to find an appealing middle ground between nonadaptive agent programming languages and systems that solely learn user preferences from training examples. On one hand, ....

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Towell G.G., Shavlik J.W. (1994). Knowledge-Based Artificial Neural Networks. Artificial Intelligence, 70, 119-165.

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Towell, G., Shavlik, J.: Knowledge-based Artificial Neural Networks. Artificial Intelligence, Vol. 70. (1994)

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G. Towell and J. Shavlik, "Knowledge-based artificial neural networks, " Artif. Intell., vol. 70, pp. 119--165, 1994.

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Towell, G., Shavlik, J.: Knowledge-based artificial neural networks. Artificial Intelligence 70 (1994)

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G. G. Towell and J. W. Shavlik. Knowledge-based artificial neural networks. Artificial Intelligence, 70(1--2):119--165, 1994.

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Geoffrey G. Towell and Jude W. Shavlik. Knowledge-based artificial neural networks. Artificial Intelligence, 70(1--2):119-- 165, 1994.

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G. G. Towell and J. W. Shavlik. Knowledge-based artificial neural networks. Artificial Intelligence, 70(1--2):119--165, 1994.

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G. G. Towell and J. W. Shavlik. Knowledge-Based Artificial Neural Networks. Artificial Intelligence, 70(1-2):119--165, 1994.

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G. G. Towell and J. W. Shavlik; " Knowledge-Based Artificial Neural Networks" ; Artificial Intelligence, Vol. 70; 1994.

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G. G. Towell and J. W. Shavlik; " Knowledge-Based Artificial Neural Networks" ; Artificial Intelligence, Vol. 70, pp.119-165; 1994.

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G. Towell and J. Shavlik. Knowledge-based artificial neural networks. Artificial Intelligence, 70(1--2):119--165, 1994.

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Towell G, Shavlik J. Knowledge-based artificial neural networks. Artif Intell 1994;70:119--65.

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Geo#rey G. Towell and Jude W. Shavlik. Knowledge-based artificial neural networks. Artificial Intelligence, 70(1--2):119--165, 1994.

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G. G. Towell and J. W. Shavlik. Knowledge-Based Artificial Neural Networks. Artificial Intelligence, 70(1-2):119--165, 1994.

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G. G. Towell and J. W. Shavlik, "Knowledge-based artificial neural networks, " Artif. Intell., vol. 70, pp. 119--165, 1994.

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Geo#rey G. Towell and Jude W. Shavlik. Knowledge-based artificial neural networks. Artificial Intelligence, 70(1--2):119--165, 1994.

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G. Towell and J. Shavlik, "Knowledge-based artificial neural networks, " Artif. Intell., vol. 70, pp. 119--165, 1994.

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G. Towell and J. Shavlik. Knowledge-based artificial neural networks. Artificial Intelligence, 70(1--2):119--165, 1994.

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