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.

A Class of Bounded Distributed Control Strategies for Connectivity Preservation in Multi-Agent Systems

Abstract: This technical note proposes a general class of distributed potential-based control laws with the connectivity preserving property for single-integrator agents. The potential functions are designed in such a way that when an edge in the information flow graph is about to lose connectivity, the gradient of the potential function lies in the direction of that edge, aiming to shrink it. The results are developed for a static information flow graph first, and then are extended to the case of dynamic edge addition. Connectivity preservation for problems involving static leaders is covered as well. The potential functions are chosen to be smooth, resulting in bounded control inputs. Other constraints may also be imposed on the potential functions to satisfy various design criteria such as consensus, containment, and formation convergence. The effectiveness of the proposed control strategy is illustrated by simulation for examples of consensus and containment.

Distributed Optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach

Abstract: This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results.

Environments for Multi-Agent Systems III

Abstract: The modern field of multiagent systems has developed from two main lines of earlier research. Its practitioners generally regard it as a form of artificial intelligence (AI). Some of its earliest work was reported in a series of workshops in the US dating from 1980, revealingly entitled, “Distributed Artificial Intelligence,” and pioneers often quoted a statement attributed to Nils Nilsson that “all AI is distributed.” The locus of classical AI was what happens in the head of a single agent, and much MAS research reflects this heritage with its emphasis on detailed modeling of the mental state and processes of individual agents. From this perspective, intelligence is ultimately the purview of a single mind, though it can be amplified by appropriate interactions with other minds. These interactions are typically mediated by structured protocols of various sorts, modeled on human conversational behavior. But the modern field of MAS was not born of a single parent. A few researchers have persistently advocated ideas from the field of artificial life (ALife). These scientists were impressed by the complex adaptive behaviors of communities of animals (often extremely simple animals, such as insects or even microorganisms). The computational models on which they drew were often created by biologists who used them not to solve practical engineering problems but to test their hypotheses about the mechanisms used by natural systems. In the artificial life model, intelligence need not reside in a single agent, but emerges at the level of the community from the nonlinear interactions among agents. Because the individual agents are often subcognitive, their interactions cannot be modeled by protocols that presume linguistic competence. The French biologist Grass e observed that these interactions are typically achieved indirectly, through modifications of a shared environment [1]. All interaction among agents of any sort requires an environment. For an AI agent whose interactions with other agents are based on speech act theory, the environment consists of a computer network that can convey messages from one agent’s outbox to another agent’s inbox. For an ALife agent, the environment is whatever the agent’s sensors sense and whatever its effectors try to manipulate. In most cases, AI agents (and their designers) can take the environment for granted. Error-correcting protocols ensure that messages once sent will arrive in due course. Message latency may lead to synchronization issues among agents, but these issues can be discussed entirely at the level of the agents themselves, without reasoning about the environment. As a result, the environment fades into the background, and becomes invisible. Not so for ALife agents. Simon observed long ago that the complex behavior of an ant wandering along the ground is best explained not by what goes on inside the ant, but by what happens outside, in the structure of the ground over which the ant moves [2]. When a termite interacts with other termites by depositing and sensing pheromones, the absorption and evaporation of the pheromone by the environment plays a critical role in the emergent structure of the colony’s behavior. There are no error-correcting protocols to ensure that an agent who tries to push a rock from one place to another will in fact be able to realize that objective. From the ALife perspective, the environment is an active participant in agent dynamics, a first-class member of the overall system. One happy result of the confluence of AI and ALife in MAS is the emergence of hybrid agents that draw on the best of both earlier traditions. This volume, and the workshop of which it is the archival record, is evidence of that hybridization. The agents described in these papers are not artificial ants constructed to test a biologist’s theories about insect behavior, but components of systems engineered to fly airplanes, or analyze sensor data, or produce plausible human-like behavior in a video game. Like AI agents, many of them have cognitive, symbolic internal structures. Like ALife agents, all of them interact explicitly and deliberately with the environment through which they coordinate their behaviors. The notion of the environment in MAS is still young, but the number of papers contributed to this volume suggests the potential of this concept for engineered systems, and their breadth sketches the broad framework of what a mature discipline of environments for multiagent systems might resemble. The entire life cycle of environmental engineering is represented here: conceptual models and languages for the design and specification of environments, simulation environments that admit environments as first-class objects, analysis of the role played by an explicit environment in agent coordination, and examples of full applications that exploit the power of an active environment. The introductory survey pulls these themes together to offer an integrated overview of this emerging discipline. This volume shows the wide range of exploration typical of a nascent discipline as pioneers discover the best ways to frame problems and approach solutions. It will enable other researchers to take build on this body of initial exploration, and should form the foundation for a fruitful new set of tools and methods for developing multiagent systems. [1] Grass e, P.P.: La Reconstruction du Nid et les Coordinations Inter-individuelles chez Bellicositermes Natalensis et Cubitermes sp. La theorie de la Stigmergie: Essai d’Interpre etation du Comportement des Termites Constructeurs. Insectes Sociaux 6 (1959) 41-84 [2] Simon, H.A.: The Sciences of the Artificial. Cambridge, MA, MIT Press (1969)