TL;DR
This survey develops a unified taxonomy that systematically integrates learning paradigms, agent architectures, coordination mechanisms, deployment models, application domains, and evaluation frameworks from a common analytical perspective and provides a structured foundation for future advances in intelligent agent systems.
A comprehensive framework for the design, evaluation, and responsible deployment of Agentic AI is proposed, emphasizing safety, explainability, human-in-the-loop supervision, and ethical compliance and aims to maximize the benefits of Agentic AI while minimizing potential risks.
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
Autonomous decision systems have become essential in modern intelligent computing, driven by advances in AI and distributed computing. This paper studies multi-agent AI (MAAI) systems, focusing on their theoretical foundations, design methods, and performance before 2018. Multi-agent systems enable decentralized, scalable, and adaptive decision-making by distributing intelligence among interacting agents capable of perception, reasoning, and action. The paper highlights how agent-based models integrate with decision frameworks, where cooperation, coordination, and competition lead to intelligent behavior. It reviews approaches such as rule-based systems, utility models, and reinforcement learning in multi-agent contexts, while addressing challenges like scalability, communication overhead, conflict resolution, and uncertainty. It also examines key developments in distributed AI, including contract net protocols, distributed constraint satisfaction, and game-theoretic methods, along with applications in robotics, smart grids, traffic, and defense. Finally, it discusses system evaluation metrics like efficiency, convergence, and fault tolerance, offering a consolidated reference and identifying future research directions.
Through applied case studies in pharmaceutical discovery and financial systems, common design patterns that make agentic systems successful are analyzed, and practical mitigation strategies for failure modes are discussed, such as verification pipelines, fallback mechanisms, and human-in-the-loop supervision.
This work synthesizes perspectives from philosophy, cognitive science, and AI to define agency, outline its key properties, and situate it in relation to existing paradigms such as reinforcement learning, symbolic reasoning, Belief–Desire–Intention (BDI) architectures, and embodied cognition.
This systematic review explores the intersection between Multi-Agent Systems and Digital Twins, with a particular focus on predictive maintenance applications in resource-constrained contexts and reveals that, despite significant progress, no existing system offers an integrated embedded-distributed hierarchical solution that simultaneously meets the requirements of Industry 5.0.
Education Innovation
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
General Science
Climate & Sustainability
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