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 Abductive Reasoning Optimization Using Recurrent Neural Networks. Theory, solution architecture and implementation techniques
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Abductive Reasoning Optimization Using Recurrent Neural Networks. Theory, solution architecture and implementation techniques

автор Emad A. издательство Книга по Требованию Локальные и корпоративные сети
Cтраниц 136        мягкая обложка       
Аннотация:Current methods suffer from exponential complexity, in the worst case. Reasoning under uncertainty is a unique capability of human beings. The optimal solution for a given CBA instance, which is the best explanation, is the one associated with the Least Cost Proof (LCP). CBA is an important AI formalism for representing knowledge under uncertainty to enable us to choose among those explanations. In CBA, the data to be explained is treated as a goal that is necessarily true, and it is to be proven through a set of assumable hypotheses. Abduction is “Inference to the best explanation” which may result in many explanations. Finding LCP for a given CBA system is NP-Hard. That requires using more expressive forms of logic like higher-order logic, or using numeric representations for uncertainty like Bayesian Networks (BN), or Cost-Based Abduction (CBA). Reasoning under uncertainty in AI is concerned with automated reasoning using contradicting or inconsistent information. Our work shows that HORN is a very promising method for solving NP-Hard problems. This book, therefore, provides a novel scalable and noise tolerant method using High Order Recurrent Neural Network (HORN) to solve CBA.
Автор: Emad A.
Издательство: Книга по Требованию
Год:2010
Цена:
2415 руб
Нет в наличии
Поиск: Abductive Reasoning Optimization Using Recurrent Neural Networks. Theory, solution architecture and implementation techniques


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