Abstract
The aim of behavioural cloning is to synthesize artificial controllers that are robust and comprehensible to human understanding. To attain the two objectives we propose the use of the Incremental Correction model that is based on a closed-loop control strategy to model the reactive aspects of human control skills. We have investigated the use of three different representations to encode the artificial controllers: univariate decision trees as induced by C4.5; multivariate decision and regression trees as induced by cart and; clausal theories induced by an Inductive Logic Programming (ILP) system. We obtained an increase in robustness and a lower complexity of the controllers when compared with results using other models. The controllers synthesized by cart revealed to be the most robust. The ILP system produced the simpler encodings. © Springer-Verlag Berlin Heidelberg 2001.

Abstract
The paper begins with the discussion on why we should be concerned with machine learning in the context of distributed AI. The rest of the paper is dedicated to various problems of multi-agent learning. First, a common framework for comparing different existing systems is presented. It is pointed out that it is useful to distinguish when the individual agents communicate. Some systems communicate during the learning phase, others during the problem solving phase, for example. It is also important to consider how, that is in what language, the communication is established. The paper analyses several systems in this framework. Particular attention is paid to previous work done by the authors in this area. The paper covers use of redundant knowledge, knowledge integration, evaluation of hypothesis by a community of agents and resolution of language differences between agents. © Springer-Verlag Berlin Heidelberg 1991.

Abstract

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Abstract
Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature space transformation whereas some others compared the performance of different algorithms. Recently, following the rising interest towards the Support Vector Machine, various studies showed that SVM outperforms other classification algorithms. So should we just not bother about other classification algorithms and opt always for SVM ? We have decided to investigate this issue and compared SVM to kNN and naive Bayes on binary classification tasks. An important issue is to compare optimized versions of these algorithms, which is what we have done. Our results show all the classifiers achieved comparable performance on most problems. One surprising result is that SVM was not a clear winner, despite quite good overall performance. If a suitable preprocessing is used with kNN, this algorithm continues to achieve very good results and scales up well with the number of documents, which is not the case for SVM. As for naive Bayes, it also achieved good performance.

Abstract
Inductive Logic Programming (ILP) is concerned with construction of logic programs from examples. It shares many concerns of Machine Learning (ML), but is committed to logic. As logic can help to provide a basis for elaborating such a methodology for learning, the area of ILP has attracted a wide attention of many researchers1. This paper reviews some of the methods and techniques in ML that exploit logic.