Models of incremental concept formation

Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling this type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions

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