The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) a distributed of structured and linked information. At its core, concept ontology provides means semantically describe structure information, expose it software human agents in machine human-readable form. For be realized, is crucial develop powerful artificial intelligence machine-learning techniques, a...

A current trend of research focuses on artificial intelligence based cryptography which although proposed almost thirty years ago could not attract much attention. Abadi and Anderson’s work adversarial in 2016 rejuvenated the area now building neural networks that are able to learn using idea from Generative Adversarial Networks (GANs). In this paper, we survey most prominent works cover two ma...

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys integration of both fields, better known model-based learning. Model-based RL has two main steps: dynamics model planning-learning integration. ...

Documenting whether a biotic taxon is native or alien to an ecosystem has theoretical value for ecological and evolutionary studies, and has practical value because it can potentially identify a taxon as a desirable component of an ecosystem or target it for removal. In some cases, however, such background information is inadequate or unavailable. Here we use paleo-DNA to re-evaluate the histor...