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.

Transport modeling by multi-agent systems: a swarm intelligence approach

Abstract: There are a number of emergent traffic and transportation phenomena that cannot be analyzed successfully and explained using analytical models. The only way to analyze such phenomena is through the development of models that can simulate behavior of every agent. Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual ‘agents’ and that each agent interacts with other agents according to localized knowledge. The agent-based approach is a ‘bottom-up’ approach to modeling where special kinds of artificial agents are created by analogy with social insects. Social insects (including bees, wasps, ants and termites) have lived on Earth for millions of years. Their behavior in nature is primarily characterized by autonomy, distributed functioning and self-organizing capacities. Social insect colonies teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. On the other h...

A Supermodular Optimization Framework for Leader Selection Under Link Noise in Linear Multi-Agent Systems

Abstract: In many applications of multi-agent systems (MAS), a set of leader agents acts as control inputs to the remaining follower agents. In this paper, we introduce an analytical approach to selecting leader agents in order to minimize the total mean-square error of the follower agent states from their desired value in steady-state in the presence of noisy communication links. We show that, for a set of link weights based on the second-order noise statistics, the problem of choosing leaders in order to minimize this error can be solved using supermodular optimization techniques, leading to efficient algorithms that are within a provable bound of the optimum. We formulate two leader selection problems within our framework, namely the problem of choosing a fixed number of leaders to minimize the error, as well as the problem of choosing the minimum number of leaders to achieve a tolerated level of error. We study both leader selection criteria for different scenarios, including MAS with static topologies, topologies experiencing random link or node failures, switching topologies, and topologies that vary arbitrarily in time due to node mobility. In addition to providing provable bounds for all of these cases, simulation results demonstrate that our approach outperforms other leader selection methods, such as node degree-based and random selection methods, and provides comparable performance to current state of the art algorithms.

The AMAS theory for complex problem solving based on self-organizing cooperative agents

Abstract: In this paper, we present an approach for the design of complex adaptive systems, based on adaptive multi-agent systems and emergence. We expound the AMAS theory (Adaptive Multi-Agent Systems) and its technical working. This theory gives local agent design criteria so as to enable the emergence of an organization within the system and thus, of the global function of the system. We also present the theorem of functional adequacy witch ensures that a cooperative self organizing system performs a suitable work. Applications of this theory in the multi-agent system framework led us to define the architecture and a general algorithm for cooperative agents. The originality of our approach lies in the very generic manner our re-organization rules work and that they are completely independent from the function the system has to compute.