Five eighth grade science classes of students in at a middle school were assigned to three treatment groups: those who individually concept mapped, those who collaboratively concept mapped, and those who independently used their study time. The findings revealed that individually generating concept maps on computers during study time positively influenced science concept learning above and beyo...

This thesis consists of three overlapping parts, where the first one centers around the possibility of defining a measure of the power of arithmetical theories. In this part a partial measure of the power of arithmetical theories is constructed, where “power” is understood as capability to prove theorems. It is also shown that other suggestions in the literature for such a measure do not satisf...

In formal concept analysis of data with fuzzy attributes, both the extent and the intent of a formal (fuzzy) concept may be fuzzy sets. In this paper we focus on so-called crisply generated formal concepts. A concept 〈A,B〉 ∈ B (X,Y, I) is crisply generated if A = D↓ (and so B = D↓↑) for some crisp (i.e., ordinary) set D ⊆ Y of attributes (generator). Considering only crisply generated concepts ...

It has been observed that a careful selection of training examples improves the performance of supervised concept learning systems. This observation is important when examples are not available and are expensive to generate, or when using incremental learning systems. These two reasons are manifested in the context of BRIDGER, a system that learns to design cable-stayed bridges. This paper desc...

Explanation-based generalization (EBG) is a powerful approach to concept formation in which a justifiable concept definition is acquired from a single training example and an underlying theory of how the example is an instance of the concept. Soar is an attempt to build a general cognitive architecture combining general learning, problem solving, and memory capabilities. It includes an independ...

Context Concept formation is the process by which generalizations are formed through observation of instances from the environment. These instances are described along a number of attributes, which are selected according to their relevance to the problem or task for which the concepts will be used. The context of a concept learning problem consists of the goals and tasks of the learner, as well...

We study learning scenarios in which multiple learners are involved and “nature” imposes some constraints that force the predictions of these learners to behave coherently. This is natural in cognitive learning situations, where multiple learning problems co-exist but their predictions are constrained to produce a valid sentence, image or any other domain representation. Our theory addresses tw...

In this paper, we firstly describe a novel approach to calculate the Chinese sememe similarity based on the HowNet hierarchical sememe tree. When we calculate the sememe similarity, we not only take Semantic Distance, Node Depth and Semantic Coincidence Degree into consideration, but also propose two impact factors named Node Environment Dense (NED) and Node Layer Ratio (NLR) to optimize the ca...

Future computer vision systems must have the ability to discover new object models. This problem can be addressed by relational concept formation systems, which structure a stream of observations into a taxonomy of discovered concepts. This paper presents a representation for images which is invariant under arbitrary groups of transformations. The discovered models, also being invariant, can be...

Concept learning belongs to the fundamental challenges of educational technology. Contrary to the current trend of designing and implementing contents for various web–based virtual courses, concept learning requires cognitive tools rather than digital materials. We introduce a novel scheme to generate computer games from concept maps made by a teacher or a learner herself. Concept gaming exceed...