Formal Methods For Multi Agent Feedback Control Systems
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Formal Methods for Multi-Agent Feedback Control Systems
"This book will be the first to bridge the gap between the field of formal methods and safety-critical control in cyber-physical systems"--
Formal Methods for Multi-Agent Feedback Control Systems
An introduction to formal methods for feedback control of multi-agent systems with safety and performance guarantees. Multi-agent control systems can accomplish tasks that single-agent systems cannot address, such as aerial surveillance of large areas by a group of drones. In Formal Methods for Multi-Agent Feedback Control Systems, Lars Lindemann and Dimos Dimarogonas provide an accessible introduction to formal methods for feedback control of multi-agent systems. Their book is the first to bridge the gap between formal methods and feedback control for the scalable design of cyber-physical systems. The material covered is intended for scientists, engineers, and students, and no background in formal methods or control theory is required. The authors also highlight future research directions for those working at the intersection of formal methods and control. In control theory, the goal is to design feedback control laws for dynamical systems that achieve control objectives such as stability or forward invariance of sets. Formal methods, on the other hand, provide verification and design techniques for more complex system specifications using temporal logics. However, their high computational cost limits scaling beyond a small number of agents. Besides scalability, another central challenge is to achieve robustness in the system design. Thus, the authors focus on the design of scalable and robust feedback control algorithms for multi-agent control systems under temporal logic specifications.
Multiple Approaches to Intelligent Systems
We never create anything, We discover and reproduce. The Twelfth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems has a distinguished theme. It is concerned with bridging the gap between the academic and the industrial worlds of Artificial Intelligence (AI) and Expert Systems. The academic world is mainly concerned with discovering new algorithms, approaches, and methodologies; however, the industrial world is mainly driven by profits, and concerned with producing new products or solving customers’ problems. Ten years ago, the artificial intelligence research gap between academia and industry was very broad. Recently, this gap has been narrowed by the emergence of new fields and new joint research strategies in academia. Among the new fields which contributed to the academic-industrial convergence are knowledge representation, machine learning, searching, reasoning, distributed AI, neural networks, data mining, intelligent agents, robotics, pattern recognition, vision, applications of expert systems, and others. It is worth noting that the end results of research in these fields are usually products rather than empirical analyses and theoretical proofs. Applications of such technologies have found great success in many domains including fraud detection, internet service, banking, credit risk and assessment, telecommunication, etc. Progress in these areas has encouraged the leading corporations to institute research funding programs for academic institutes. Others have their own research laboratories, some of which produce state of the art research.