Inductive reasoning is one of the important characteristics of human intelligence. Researchers have regarded inductive reasoning as one of the seven primary mental abilities that are accounted for intelligent behaviours; researches also showed that it is the best predictor for academic performance. Despite its recognised importance underlying the learning process of human beings, little effort ...

Our aim is to explain a general theory of inductive reasoning which is close enough to the concerns of language studies. In this setup , the optimal prediction rate is assigned to the hypothesis considered most likely by a prior-free form of Bayesian inference. In terms of practical applications a most attractive form of this approach is embodied by the so-called minimum description length (MDL...

Inductive reasoning entails using existing knowledge or observations to make predictions about novel cases. We review recent findings in research on category-based induction as well as theoretical models of these results, including similarity-based models, connectionist networks, an account based on relevance theory, Bayesian models, and other mathematical models. A number of touchstone empiric...

Inductive reasoning under risk conditions is an important thinking process not only for sciences but also in our daily life. From this viewpoint, it is very useful for language learning to construct computational models of inductive reasoning which realize the CAE for foreign languages. This study proposes the comparison of inductive reasoning under risk conditions between Chinese and Japanese ...

Our ability to induce the general from the specific is a hallmark of human cognition. Inductive reasoning tasks ask participants to determine how strongly a set of premises (e.g., Collies have sesamoid bones) imply a conclusion (Dogs have sesamoid bones). Here, we present evidence for an abductive theory of inductive reasoning, according to which inductive strength is determined by treating the...

Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Despite the importance of inductive relation prediction, most previous works are limited to a transductive setting and cannot process previously unseen The recent proposed subgraph-based reasoning models provided alternatives predict links from subgraph structure surrounding candidate triplet ind...

A sketch of classification reasoning is given in the paper. The key ideas of the reasoning are ideas of classification and its good approximations based on good diagnostic tests. Such good tests, which are maximally redundant (GMRTs), i.e. their subsets of attributes are closed, are considered. Classification reasoning embraces two interrelated processes: inductive inferring implicative asserti...