Download Explanation-Based Neural Network Learning: A Lifelong by Sebastian Thrun PDF

By Sebastian Thrun

Lifelong studying addresses occasions within which a learner faces a sequence of other studying projects delivering the chance for synergy between them. Explanation-based neural community studying (EBNN) is a laptop studying set of rules that transfers wisdom throughout a number of studying initiatives. whilst confronted with a brand new studying activity, EBNN exploits area wisdom collected in earlier studying initiatives to lead generalization within the new one. accordingly, EBNN generalizes extra effectively from much less facts than related tools. Explanation-Based Neural community studying: A Lifelong LearningApproach describes the elemental EBNN paradigm and investigates it within the context of supervised studying, reinforcement studying, robotics, and chess.
`The paradigm of lifelong studying - utilizing previous discovered wisdom to enhance next studying - is a promising course for a brand new new release of computer studying algorithms. Given the necessity for extra actual studying tools, it really is tricky to visualize a destiny for computer studying that doesn't comprise this paradigm.'
From the Foreword by way of Tom M. Mitchell.

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Extra resources for Explanation-Based Neural Network Learning: A Lifelong Learning Approach

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The SOAR architecture automatically records this explanation whenever it reflects in this fashion, and the chunking mechanism forms a new control rule (called a production) by collecting the features mentioned in the explanation into the preconditions of the new rule. SOAR's analytical chunking mechanism has been shown to learn successfully to speed up problem solving across a broad range of domains. For example, [60] presents results in which over 100,000 productions are learned from such explanations within one particular domain.

In some cases, intermediate concepts of the explanation are directly observable. In this case, the prediction accuracy can be estimated independently for each individual domain theory network. EBNN assigns an individual error value dj (p) to each individual inference step j. Now assume the slope is a product of m(p) chained slopes, each having its own error dj(p) (j = 1, ... , m). 17) max This product assigns large weight only to slopes where every domain theory network exhibits a small prediction error.

On the other hand, they typically require large amounts Explanation-Based Neural Network Learning 21 (a) Target concept. open_vessel/\ flaLbottom /\ [( is_light 1\ has-handle) V (made_of_Styrofoam 1\ upward_concave)) (b) Training examples. IS light has handle made of Styrofoam color upward concave open vessel fiat bottom IS expensive . v IS_CUp. 1 Cup example. (a) The target concept. (b) Training examples. of training data to generalize correctly. To illustrate this, consider the cup example. 1.

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