Multi-agent system

About: Multi-agent system is a research topic. Over the lifetime, 27978 publications have been published within this topic receiving 465191 citations. The topic is also known as: multi-agent systems & multiagent system.

Introduction to planning in multiagent systems

Abstract: In most multiagent systems planning on forehand can help to seriously improve the efficiency of executing actions. The main difference between centrally creating a plan and constructing a plan for a system of agents lies in the fact that in the latter coordination plays the main part. This introduces a number of additional difficulties. This special issue discusses some of these difficulties in detail. To place these in a context, this introduction gives a brief overview of multiagent planning problems, and most multiagent planning techniques.

Decision Making in Multiagent Systems: A Survey

Abstract: Intelligent transport systems, efficient electric grids, and sensor networks for data collection and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve common goals. Decision making is an integral part of intelligent agents and MAS that will allow such systems to accomplish increasingly complex tasks. In this survey, we investigate state-of-the-art work within the past five years on cooperative MAS decision making models, including Markov decision processes, game theory, swarm intelligence, and graph theoretic models. We survey algorithms that result in optimal and suboptimal policies such as reinforcement learning, dynamic programming, evolutionary computing, and neural networks. We also discuss the application of these models to robotics, wireless sensor networks, cognitive radio networks, intelligent transport systems, and smart electric grids. In addition, we define key terms in the area and discuss remaining challenges that include incorporating big data advancements to decision making, developing autonomous, scalable and computationally efficient algorithms, tackling more complex tasks, and developing standardized evaluation metrics. While recent surveys have been published on this topic, we present a broader discussion of related models and applications. Note to Practitioners: Future smart cities will rely on cooperative MAS that make decisions about what actions to perform that will lead to the completion of their tasks. Decision making models and algorithms have been developed and reported in the literature to generate such sequences of actions. These models are based on a wide variety of principles including human decision making and social animal behavior. In this paper, we survey existing decision making models and algorithms that generate optimal and suboptimal sequences of actions. We also discuss some of the remaining challenges faced by the research community before more effective MAS deployment can be achieved in this age of Internet of Things, robotics, and mobile devices. These challenges include developing more scalable and efficient algorithms, utilizing the abundant sensory data available, tackling more complex tasks, and developing evaluation standards for decision making.

Holonic multiagent systems: A foundation for the organisation of multiagent systems

Abstract: With the growing usage of the world-wide ICT networks, agent technologies and multiagent systems are attracting more and more attention, as they perform well in environments that are not necessarily well-structured and benevolent. Looking at the problem solving capacity of multiagent systems, emergent system behaviour is one of the most interesting phenomena, however, there is more to multiagent systems design than the interaction between a number of agents: For an effective system behaviour we need structure and organisation. But the organisation of a multiagent systems is difficult to specify at design time in the face of a changing environment. This paper presents basic concepts for a theory of holonic multiagent systems to both provide a methodology for the recursive modelling of agent groups, and allow for dynamic reorganisation during runtime.

Agent-Based Control Framework for Distributed Energy Resources Microgrids

Abstract: Distributed energy resources (DERs) provide many benefits for the electricity users and utilities. However, the electricity distribution system traditionally was not designed to accommodate active power generation and storage at the distribution level. The microgrid provides an effective approach to integrating many small-scale distributed energy resources into the bulk electric grid. This paper presents an agent-based control framework for distributed energy resources microgrids. The features of agent technology are first discussed. An agent-based control framework for DER microgrids is then presented. To demonstrate the effectiveness of the proposed agent-based control framework, simulation studies have been performed on a dc distributed energy system that can be used in a microgrid as a modular power generation unit. Simulation results clearly indicate that the agent-based control framework is effective to coordinate the various distributed energy resources and manage the power and voltage profiles.