Knowledge representation issues in control knowledge learning

Seventeenth International Conference on Machine Learning. Stanford, CA, USA, 29 June-2 July, 2000Knowledge representation is a key issue for any machine learning task. There have already been many comparative studies about knowledge representation with respect to machine learning in classication tasks. However, apart from some work done on reinforcement learning techniques in relation to state representation, very few studies have concentrated on the eect of knowledge representation for machine learning applied to problem solving, and more specically, to planning. In this paper, we present an experimental comparative study of the eect of changing the input representation of planning domain knowledge on control knowledge learning. We show results in two classical domains using three dierent machine learning systems, that have previously shown their eectiveness on learning planning control knowledge: a pure ebl mechanism, a combination of ebl and induction (hamlet), and a Genetic Programming based system (evock).Publicad

Crossing levels:meta-induction and the problem of induction

Gerhard Schurz claims to have a solution to Hume’s problem of induction based on results from machine learning concerning meta-induction. His argument has two steps. The first is to establish a justification for following a certain meta-inductive strategy based on its predictive optimality. The second step is to show how this justification can be transferred to object-induction. I unpack the second step and fail to find a convincing argument supporting the transfer of justification from meta-induction to object-induction. My conclusion is that the problem of induction has not yet been solved by appeal to meta-induction

Crossing levels:meta-induction and the problem of induction

Gerhard Schurz claims to have a solution to Hume’s problem of induction based on results from machine learning concerning meta-induction. His argument has two steps. The first is to establish a justification for following a certain meta-inductive strategy based on its predictive optimality. The second step is to show how this justification can be transferred to object-induction. I unpack the second step and fail to find a convincing argument supporting the transfer of justification from meta-induction to object-induction. My conclusion is that the problem of induction has not yet been solved by appeal to meta-induction

Meta-Learning for Phonemic Annotation of Corpora

We apply rule induction, classifier combination and meta-learning (stacked classifiers) to the problem of bootstrapping high accuracy automatic annotation of corpora with pronunciation information. The task we address in this paper consists of generating phonemic representations reflecting the Flemish and Dutch pronunciations of a word on the basis of its orthographic representation (which in turn is based on the actual speech recordings). We compare several possible approaches to achieve the text-to-pronunciation mapping task: memory-based learning, transformation-based learning, rule induction, maximum entropy modeling, combination of classifiers in stacked learning, and stacking of meta-learners. We are interested both in optimal accuracy and in obtaining insight into the linguistic regularities involved. As far as accuracy is concerned, an already high accuracy level (93% for Celex and 86% for Fonilex at word level) for single classifiers is boosted significantly with additional error reductions of 31% and 38% respectively using combination of classifiers, and a further 5% using combination of meta-learners, bringing overall word level accuracy to 96% for the Dutch variant and 92% for the Flemish variant. We also show that the application of machine learning methods indeed leads to increased insight into the linguistic regularities determining the variation between the two pronunciation variants studied.Comment: 8 page

Machine Learning Based Localization and Classification with Atomic Magnetometers

We demonstrate identification of position, material, orientation, and shape of objects imaged by a ⁸⁵Rb atomic magnetometer performing electromagnetic induction imaging supported by machine learning. Machine learning maximizes the information extracted from the images created by the magnetometer, demonstrating the use of hidden data. Localization 2.6 times better than the spatial resolution of the imaging system and successful classification up to 97% are obtained. This circumvents the need of solving the inverse problem and demonstrates the extension of machine learning to diffusive systems, such as low-frequency electrodynamics in media. Automated collection of task-relevant information from quantum-based electromagnetic imaging will have a relevant impact from biomedicine to security