The general objective of the research is to determine advances related Generative Artificial Intelligence. Methodology, in this research, 47 documents have been selected, carried out period 2014 - 2023; including: scientific articles, review articles and information from websites recognized organizations. Results, Intelligence demonstrating its importance various human activities, making it nec...

Machine learning approaches to computational creativity learn a generative model from a set of exemplars. We introduce combinatorial meta search, an approach for manipulating and combining different learned generative models. We hypothesize that combinatorial meta search can discover new generative models for which data may never have existed and thus expand the space of possible creative artif...

Many generative methods in arts and science are available. This paper presents a review of various generative methods oriented to the building of virtual objects useful in virtual reality applications. Grammars, automata, and evolutionary strategies are some classes of generative methods. The paper is dedicated both to Alan Turing and John von Neumann.

The present study was an attempt to investigate the acquisition of negationproperties by Persian monolingual and Kurdish-Persian bilingual learners of Englishacross different levels of language proficiency and within a generative framework.Generative models are generally concerned with issues such as universal grammar(UG), language transfer, and morphological variability in nonprimary languaged...

Despite recent advances, the remaining bottlenecks in deep generative models are necessity of extensive training and difficulties with generalization from small number of training examples. Both problems may be addressed by conditional generative models that are trained to adapt the generative distribution to additional input data. So far this idea was explored only under certain limitations su...

I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesi...

This paper aims to give a hierarchical, generative account of diatonic harmony progressions and proposes a generative phrase-structure grammar. The formalism accounts for structural properties of key, functional, scale and surface level. Being related to linguistic approaches in generative syntax and to the hierarchical account of tonality in the generative theory of tonal music (GTTM) [1], cad...