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.

The Multi-Agent Rendezvous Problem. Part 2: The Asynchronous Case

Abstract: This paper is concerned with the collective behavior of a group of $n>1$ mobile autonomous agents, labelled $1$ through $n$, which can all move in the plane. Each agent is able to continuously track the positions of all other agents currently within its “sensing region,” where by an agent's sensing region we mean a closed disk of positive radius $r$ centered at the agent's current position. The multi-agent rendezvous problem is to devise “local” control strategies, one for each agent, which without any active communication between agents cause all members of the group to eventually rendezvous at a single unspecified location. This paper describes a family of unsynchronized strategies for solving the problem. Correctness is established appealing to the concept of “analytic synchronization.”

Bipartite Tracking Consensus of Linear Multi-Agent Systems With a Dynamic Leader

Abstract: In this brief, the distributed bipartite tracking consensus problem for linear multi-agent systems (MASs) in the presence of a single leader is investigated. Compared with some related works on this topic, the leader’s control inputs in the present MAS model are allowed to be nonzero and unknown to each follower. To guarantee bipartite tracking consensus, a new kind of distributed non-smooth protocols based on the relative state information of neighboring agents are proposed and analyzed. With the help of tools from Lyapunov stability theory and graph theory, it is shown that bipartite tracking consensus in the close-loop MAS can be achieved if the gain matrix of protocol is suitably constructed and the control parameters of protocol are, respectively, larger than some positive quantities depending on global information of the considered MAS. To provide some efficient criteria for consensus seeking without involving any global information, some fully distributed protocols with adaptive control parameters are further designed and discussed. Finally, numerical simulations are given for illustration.

Agent-based modeling in ecological economics.

Abstract: Interconnected social and environmental systems are the domain of ecological economics, and models can be used to explore feedbacks and adaptations inherent in these systems. Agent-based modeling (ABM) represents autonomous entities, each with dynamic behavior and heterogeneous characteristics. Agents interact with each other and their environment, resulting in emergent outcomes at the macroscale that can be used to quantitatively analyze complex systems. ABM is contributing to research questions in ecological economics in the areas of natural resource management and land-use change, urban systems modeling, market dynamics, changes in consumer attitudes, innovation, and diffusion of technology and management practices, commons dilemmas and self-governance, and psychological aspects to human decision making and behavior change. Frontiers for ABM research in ecological economics involve advancing the empirical calibration and validation of models through mixed methods, including surveys, interviews, participatory modeling, and, notably, experimental economics to test specific decision-making hypotheses. Linking ABM with other modeling techniques at the level of emergent properties will further advance efforts to understand dynamics of social-environmental systems.

Achieving Cluster Formation of Multi-Agent Systems Under Aperiodic Sampling and Communication Delays

Abstract: This paper addresses the problem of cluster formation control for a networked multi-agent system (MAS) in the simultaneous presence of aperiodic sampling and communication delays. First, to fulfill multiple formation tasks, a group of agents are decomposed into $M$ distinct and nonoverlapping clusters. The agents in each cluster are then driven to achieve a desired formation, whereas the MAS as a whole accomplishes $ M $ cluster formations. Second, by a proper modeling of aperiodic sampling and communication delays, an aperiodic sampled-data cluster formation protocol (CFP) is delicately constructed such that the information exchanges among neighboring agents only occur intermittently at discrete instants of time. Third, a detailed theoretical analysis of cluster formability is carried out and a sufficient and necessary condition is provided such that the system is $M$ -cluster formable. Furthermore, a discontinuous Lyapunov functional approach is developed to derive a design criterion on the existence of an admissible sampled-data CFP. Finally, numerical simulations on a team of nonholonomic mobile robots are given to illustrate the effectiveness of the obtained theoretical result.

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

Abstract: The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence and multiagent systems in particular As it is often the case, it is not possible to provide additional capacity, so that a more efficient use of the available transportation infrastructure is necessary This relates closely to multiagent systems as many problems in traffic management and control are inherently distributed Also, many actors in a transportation system fit very well the concept of autonomous agents: the driver, the pedestrian, the traffic expert; in some cases, also the intersection and the traffic signal controller can be regarded as an autonomous agent However, the "agentification" of a transportation system is associated with some challenging issues: the number of agents is high, typically agents are highly adaptive, they react to changes in the environment at individual level but cause an unpredictable collective pattern, and act in a highly coupled environment Therefore, this domain poses many challenges for standard techniques from multiagent systems such as coordination and learning This paper has two main objectives: (i) to present problems, methods, approaches and practices in traffic engineering (especially regarding traffic signal control); and (ii) to highlight open problems and challenges so that future research in multiagent systems can address them