Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating
agents that control autonomous systems. However, the “black box” nature of DRL agents …

Deep reinforcement learning agents have achieved unprecedented results when learning to
generalize from unstructured data. However, the “black-box” nature of the trained DRL …

Can We Prevent a Technological Arms Race in University Student Cheating? Page 1 90
COMPUTER PUBLISHED BY THE IEEE COMPUTER SOCIETY 0018-9162/21©2021IEEE …

ScenarioTools is an Eclipse-based tool suite for the scenario-based modeling and analysis
of reactive systems. ScenarioTools especially targets the modeling and analysis of systems …

Deep reinforcement learning has proven remarkably useful in training agents from
unstructured data. However, the opacity of the produced agents makes it difficult to ensure …

The manual modeling of complex systems is a daunting task; and although a plethora of
methods exist that mitigate this issue, the problem remains very difficult. Recent advances in …

Scenario-Based Programming is a methodology for modeling and constructing complex
reactive systems from simple, stand-alone building blocks, called scenarios. These …

The article describes a calculation model for the development of the transport complex in the
diversified structure of the Russian economy. The model is a set of variables describing the …

Robots are found in most, if not all, modern production facilities and they increasingly enter
other domains, eg, health care. Robots participate in complex processes and often need to …

Software systems become increasingly distributed and interconnected. Single functions are
usually realized by the interaction of multiple components, while single components …