TL;DR: The learning method presented here is a general data-driven method that learns multiple discriminant disjunctive descriptions incrementally from experiments assuming perfect classifications.
Abstract: The learning method presented here is a general data-driven method that learns multiple discriminant disjunctive descriptions incrementally from experiments assuming perfect classifications. New points are selected for classification by the environment based on the current concept descriptions. Unlike previous methods for acquiring concepts, attributes with finite unordered and infinite totally-ordered domains are integrated into a uniform framework in which concept descriptions are not only constrained by negative examples, but, more importantly, by the current descriptions of other the classes.
TL;DR: McKnight et al. as discussed by the authors examined the effects of two teaching techniques on college liberal arts majors' ability to solve calculus problems: 1) a nontraditional method focusing on peer tutoring through heterogeneous grouping, and 2) a nonsmooth learning environment.
Abstract: teaching of content area reading and McKnight is involved in new techniques of teaching math. Both are at the University of Southwestern Louisiana, Lafayette. ■ Secondary and postsecondary mathematics instructors are generally concerned with two objectives: teaching students to understand and apply mathematical concepts, and fostering independent reasoning and transfer of learning to novel situations, based on acquired concepts and problem solving techniques. Mathematics instructors complain that students today frequently demonstrate overdependence on instructor lectures for acquiring concepts, marked difficulty comprehending mathematics textbooks, and noticeable deficits in developing problem solving techniques transferable to novel situations. While many students possess a vast array of fragmented mathematical facts, committed to memory by rote, they are unable to apply appropriate portions to the solving of particular problems. This investigation was designed to examine the effects of two teaching techniques on college liberal arts majors' ability to solve calculus problems: 1) a nontraditional method focusing on peer tutoring through heterogeneous grouping, combined
TL;DR: The authors argue in favour of RCN by developing a speculative account of concept acquisition, which has considerable nativist credentials and can be defended against the most familiar anti-nativist objections.
Abstract: Radical Concept Nativism (RCN) is the doctrine that most of our concepts are innate. In this paper I will argue in favour of RCN by developing a speculative account of concept acquisition that has considerable nativist credentials and can be defended against the most familiar anti-nativist objections. The core idea is that we have a whole battery of hard-wired dispositions that determine how we group together objects with which we interact. In having these dispositions we are effectively committed to an implicit conceptual scheme and acquiring concepts is a matter of labelling the elements of that scheme.
TL;DR: In this article, the authors explore when, where, and how one learns this complex ability to correctly categorize so many objects and events, which can be thought of as an example of problem solving.
Abstract: A concept is a symbol that stands for a class of objects or events that possess common attributes. Concepts can be verbal or nonverbal, simple or complex, and are related to one another in complicated ways. They help to think. Concept formation, of particular concern to learning psychologists, refers to how one goes about learning or acquiring concepts. Specifically, psychologists are interested in understanding how one learns to identify objects or events as examples of particular concepts. When one sees a baby whom one has never seen before, one readily identifies the baby as an example of the concept “infant.” This chapter explores when, where, and how one learns this complex ability to correctly categorize so many objects and events. Concept formation can be thought of as an example of problem solving. The problem is to learn the concept or to acquire the ability to identify correctly examples of the concept.
TL;DR: Through empirical evaluation, it is shown that the technique reduces the cost of acquiring concepts that are regularly used and reduces the complexity of the agent’s ontology by augmenting it with selected concepts and relationships which are related to its domain.
Abstract: We present a technique that enables a software agent to augment its ontology with domain related concepts by collaborating with other agents. The collaborating agents have their own individual ontologies, they can share concepts and relationships that relate to a requested specific concept (which is known as a fragment). Thus, specifically, our technique selects the fragments that will be shared. This approach enables agents to answer queries with more range and detail, and it also enables an agent to infer new exploitable knowledge. Without this capability, an agent may be limited by its domain model, and cannot reflect changes in the environment. Through empirical evaluation, we show that our technique reduces the cost of acquiring concepts that are regularly used (compared with learning nothing) and reduces the complexity of the agent’s ontology by augmenting it with selected concepts and relationships which are related to its domain (compared with learning everything).