reinforcement learning

Exploring the Landscape of Cloud Robotics: A Comprehensive Review

Cloud robotics is an innovative field that leverages cloud technologies-including cloud computing (CC), cloud storage, deep learning, big data, and the Internet of Things to augment the capabilities of robotics. This integration facilitates the execution of robotic functions through a converged infrastructure and share...

Future Trends in AI, Machine Learning, and Big Data: Implications for Technical Leadership

There's no denying that Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies are profoundly changing the face of software engineering and organizational leadership. As these technologies keep evolving, the design, deployment, and management of software systems are undergoing unprecedented chan...

AI Agentic Architectures for Autonomous Data Engineering Pipelines

This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.

Policy Drift in Learning AI Agents: A Dynamical Systems Perspective on Security Degradation

Policy drift takes shape through a nonlinear differential equation - framed within the policy state space - with support from Lyapunov stability concepts alongside bifurcation methods alongside bifurcation methods, and the Intent Drift Rate appears: a concrete number per dialogue turn built as the time-based change in...

AI-driven cybersecurity in software engineering

AI-driven cybersecurity in the software engineering field is discussed, where machine learning, deep learning, natural language processing, and reinforcement learning can be applied throughout the software development lifecycle to provide increased security.

Designing Self-Healing AI Agentic Systems: A Framework for Autonomous Detection and Response

A new scientific object – the Autonomous Recovery Efficiency Score (ARES) – is introduced – a quantitative measure of autonomous resilience, as well as a supporting foundation for future autonomous self-healing AI agentic infrastructure.

Cloud-based AI systems for scalable and intelligent software applications

The speed of cloud computing and artificial intelligence, which have transformed the way software applications are designed and deployed. The cloud-based AI systems provide a scalable, adaptable, and cost-efficient solution to build intelligent systems capable of processing large amounts of data and running complicated...

Security in Multi-Agent AI Systems: Modeling Emergent Vulnerabilities via Trust Graphs

Autonomous multi-agent artificial intelligence (AI) systems have emerged as a rapidly evolving field that revolutionizes the way autonomous systems can make decisions together, collaborate on tasks, and learn, thereby opening new paradigms for distributed decision-making, task execution, and adaptive learning. The comp...

Active learning for interactive prompt clarification in safety-critical programmable logic controller code generation

Effectiveness of Management Information Systems in Elementary Schools: A Systematic Literature Review

The effectiveness of Management Information Systems (MIS) in elementary schools has become an important issue as educational governance increasingly requires efficient, accurate, transparent, and data-based management. This study aims to systematically examine the effectiveness of MIS implementation in elementary schoo...

When Does a Dark Forest Emerge? An Open Agent-Based Model of Interstellar Strategic Regimes

The Dark Forest hypothesis is usually presented as a general consequence of uncertainty, technological asymmetry and catastrophic vulnerability. This paper asks a narrower question: under which combinations of beliefs, capabilities, signalling conditions and network incentives does a Dark Forest actually emerge? A Dark...

When Does a Dark Forest Emerge? An Open Agent-Based Model of Interstellar Strategic Regimes

The Dark Forest hypothesis is usually presented as a general consequence of uncertainty, technological asymmetry and catastrophic vulnerability. This paper asks a narrower question: under which combinations of beliefs, capabilities, signalling conditions and network incentives does a Dark Forest actually emerge? A Dark...