A Bayesian framework helps address, in computational terms, what knowledge children start
with and how they construct and adapt models of the world during childhood. Within this …

The scope of human learning and development poses a radical challenge for cognitive
science. We propose that developmental theories can address this challenge by adopting …

Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

Rough set approach to reasoning under uncertainty is based on inducing knowledge
representation from data under constraints expressed by discernibility or, more generally …

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Inductive programming (IP)—the use of inductive reasoning methods for programming,
algorithm design, and software development—is a currently emerging research field. A …

We describe an approach to the inductive synthesis of recursive equations from
input/outputexamples which is based on the classical two-step approach to induction of …

In explanation-based learning, a specific problem's solution is generalized into a form that
can be later used to solve conceptually similar problems. Most research in explanation …

This paper presents an overview and a survey of logic program synthesis. Logic program
synthesis is interpreted here in a broad way; it is concerned with the following question …

Publisher Summary This chapter discusses artificial intelligence (AI) and software
engineering. It reviews the attempts to apply AI techniques to software engineering …