A comparative survey of integrated learning systems

This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework

Guidelines for the verification and validation of expert system software and conventional software: Bibliography. Volume 8

This volume contains all of the technical references found in Volumes 1-7 concerning the development of guidelines for the verification and validation of expert systems, knowledge-based systems, other AI systems, object-oriented systems, and conventional systems

Motivated Inductive Discovery

Research in machine discovery to date has tended to concentrate on the replication of particular episodes in the history of science, and more recently on the extraction of regularities from large databases. In this respect, current models of induction and discovery concentrate solely on the acquisition of knowledge, and lack the flexibility of reasoning that is necessary in a real-world changing environment. Against this backdrop, this dissertation addresses inductive reasoning, specifically based around the scientific discovery paradigm. A framework for inductive reasoning is presented which includes the six stages of prediction, experimentation, observation, evaluation, revision and selection. Within this framework, different kinds of inductive reasoning can be reduced to the same basic component processes. The difference between the various kinds of reasoning arises not through the use of different mechanisms, but through the influence of motivations which bias the application of these mechanisms accordingly. Also within this framework, a model and its implementation as a computer program, the MID system, have been developed, concentrating primarily on the internal stages of the framework, prediction, evaluation, revision and selection. The role of motivations in allowing reasoning for both knowledge and action is investigated and implemented in the program. By choosing different internal models of motivation for reasoning systems, different behaviours can be achieved from the same basic architecture. The MID system reasons in simple physical domains, both for knowledge and for action. It demonstrates how a basic mechanism can be used to provide an effective means for reasoning in a variety of contexts, and also how a simple motivational representation can be used as an effective control strategy

Theory revision via prior operationalization

Abstract. We have applied five supervised learning approaches to word sense disambiguation in the medical domain. Our objective is to evaluate Support Vector Machines (SVMs) in comparison with other well known supervised learning algorithms including the naïve Bayes classifier, C4.5 decision trees, decision lists and boosting approaches. Based on these results we introduce further refinements of these approaches. We have made use of unigram and bigram features selected using different frequency cut-off values and window sizes along with the statistical significance test of the log likelihood measure for bigrams. Our results show that overall, the best SVM model was most accurate in 27 of 60 cases, compared to 22, 14, 10 and 14 for the naïve Bayes, C4.5 decision trees, decision list and boosting methods respectively