An iterated hawk-and-dove game

Consensus conditions for heterogeneous multi-agent sampled-data control systems with nonlinearities and inter-agent communication delay.

We consider several basic consensus problems in multi-agents systems with switching interconnection graphs, where the connections between any two agents are assumed bidirectional for notation simplicity. When the switching interconnection graphs are always connected, it has been shown in the literature that the Laplacian based algorithm always achieves the average consensus. We first extend the discussion to the case where disconnected interconnection graphs are involved, by showing that the average consensus is still achieved if the dwell time ratio between connected graphs and disconnected ones satisfies a specified condition. Next, we consider the case where there is no connected graph but the combination of a set of graphs is connected, and propose two switching strategies for achieving the average consensus. Several numerical examples are provided to show the algorithms.

As a new area of research, consensus problems for multi-agent systems have been studied extensively in the literature. It is usually assumed that all agents share a common one-dimensional state space. The authors of this study concentrate on the consensus problems of multi-agent systems in the framework of high-dimensional state space. As a basis for the analysis, a general model is first introduced by presenting two typical examples along with some necessary and sufficient conditions for the system to solve a consensus problem. Then the structures of consensus functions are investigated and some algebraic characterisations are given. Finally, a class of switched multi-agent systems are discussed and some sufficient conditions for the possession of the consensus property are provided. Several examples to illustrate the effectiveness of theoretical results are given.

Game theoretical learning in multi-agent systems is a rapidly developing area of research. It gained popularity since the wide range of optimization problems in multi-agent systems can be reformulated in terms of potential games, where the set of potential function maximizers represents the set of optimal system states. The crucial point of such approach is the design of a distributed algorithm that is guaranteed to converge to the set of potential function maximizers. Various learning algorithms, whose features depend on system properties, have been proposed so far. However, there is currently no learning algorithm that can be efficiently executed in a multi-agent system in which uncoupled agents update their actions synchronously and do not take into account the past history of actions. In this paper, we fill this gap by introducing a new learning algorithm for potential games with uncoupled dynamics and memoryless players who act synchronously. We prove the probabilistic convergence of this algorithm to potential function maximizers, which correspond to the optimal system states under appropriate game settings.

We investigate the fixed-time containment control (FCC) problem of multi-agent systems (MASs) under discontinuous communication. A saturation function is used in the controller to achieve the containment control in MASs. One difference from using a symbolic function is that it avoids the differential calculation process for discontinuous functions, which further ensures the continuity of the control input. Considering the discontinuous communication, a dynamic variable is constructed, which is always non-negative between any two communications of the agent. Based on the designed variable, the dynamic event-triggered algorithm is proposed to achieve FCC, which can effectively reduce controller updating. In addition, we further design a new event-triggered algorithm to achieve FCC, called the team-trigger mechanism, which combines the self-triggering technique with the proposed dynamic event trigger mechanism. It has faster convergence than the proposed dynamic event triggering technique and achieves the tradeoff between communication cost, convergence time and number of triggers in MASs. Finally, Zeno behavior is excluded and the validity of the proposed theory is confirmed by simulation.

Due to the unbalance distribution of network resources and network traffic, congestion is an inherent property of the Internet. The consensus congestion controller based on the multiagent system theory is designed for the multirouter topology, which improves the performance of the whole networks. Based on the analysis of the causes of congestion, the topology of multirouter networks is modeled based on the graph theory and the network congestion control problem is described as a consensus problem in multiagent systems. Simulation results by MATLAB and Ns2 indicate that the proposed algorithm maintains a high throughput and a low packet drip ratio and improves the quality of the service in the complex network environment.

This report documents the programme and outcomes of the Dagstuhl Serminar 14332 Formal Methods for Coordinating Multi-Agent Systems, that took place from 10 to 14 August, 2014. This seminar brought together researchers from the following subfields of multi-agent systems: logic, game theory, and agreement technologies. It is set up at the intersection of these active fields of research and aimed at fostering collaborations between them. A key objective of the seminar has been to shed light on formal methods for coordinating multi-agent systems, in particular, how to combine research and tools from the different areas to obtain new techniques for coordinating the behavior of agents. The coordination problem is a key problem in multi-agent systems: how can we coordinate the individual behaviour of the agents such that the global behaviour of the system as a whole satisfies our needs? Dagstuhl was an excellent venue to bring together leading researchers from logics, game theory, and agreement technologies to learn about their research activities, to discuss as well as to work on timely problems, and to establish new collaborations between researchers. The outcome of the working groups and discussions provides promising avenues and open questions for future research in the field.

Game theoretic learning in multiagent systems is a rapidly developing area of research. It gained popularity since the wide range of optimization problems in multiagent systems can be reformulated in terms of potential games, where the set of potential function maximizers represents the set of optimal states. The crucial point of such approach is the design of local decision rules that leads the system to a potential function maximizer. In this paper, we consider the independent log-linear learning procedure applicable to continuous action potential games. We provide the analysis of the stochastic stability of potential function maximizers in case of continuous action games. To analyze the behavior of the underlying Markov chain, we develop a new method for approximating the initial chain with continuous states by chains whose long run properties can be established. After that, we study the finite time behavior of the algorithm with a definite time-dependent parameter and obtain settings that guarantee an efficient implementation of the learning procedure under consideration.

Exploiting the Superposition Property of Wireless Communication for Max-Consensus Problems in Multi-Agent Systems

In this manuscript, the stability and consensus problem of the leader-following multi-agent system is studied. The state information of the leader is only available to the dynamics of all the followers, while the communication among the agents occurs at sampling instant. The innovative part of this paper is to reach the consensus and attain the stability for prescribed MASs with interval time varying delay using impulsive control in the presence of leader. The interaction between the agent is illustrated by an undirected graph. A class of distributed impulsive protocol relying on sampling information is suggested to reach the leader following consensus. The consensus is critically relying on the sampling period, control gains. By performing the similar procedures, the consensus problem of multi-agent system is converted to stability problem of the error system. By utilizing the stability theory of impulsive systems, algebraic graph theory, sufficient conditions are derived to guarantee the leader-following consensus of the multi-agent system. Terminally, a numerical example is given to show an efficacy of this presented approach and the sureness of theoretical analysis.

Formation control for nonlinear multi-agent systems by robust output regulation

Exception handling is an important feature of the tool-set that enables the building of fault-tolerant concurrent and distributed programs. While transactional distributed systems have been studied for a long time, distributed exception handling techniques are only now evolving towards maturity, especially within asynchronous multi agents or component based systems. In this paper, we review two recent proposals for distributed exceptions handling systems (DEHS), namely SaGE and Guardian, in the light of what the Erlang programming language brings to the table : native constructs for concurrency, distributedness and exception handling across processes. We expand on the merits and possible drawbacks of these systems. We advocate the need to introduce the notion of resumption, an often downplayed feature of EHSs, to modern day DEHS, in order to address the problem of multi agent systems facing the “real world”.KeywordsMulti Agent SystemException HandlingService ExecutionMethod InvocationCommon LispThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Many problems in multi-agent systems can be described as Distributed Constraint Satisfaction Problems (D CSPs), where the goal is to find a set of assignments to variables that satisfies all constraints among agents. However, when real-life application problems are formalized as DCSPs, they are often over-constrained and have no solution that satisfies all constraints. Moreover, the globalization of the economy and democratization of the Internet, boosted by the huge growth in information and communication technologies, have largely contributed to the expansion of numerous distributed architectures. Thus this paper provides a new distributed management and decision support system suitable to these interdependencies and these complex environments. We present a Distributed Optimization under Constraints Basic Relax (DOC-BRelax) as a new framework for dealing with over-constrained situations. We also present a version of this framework called DOC-Max Relax and a new algorithm for solving Distributed Maximal Constraint Satisfaction Problems (DMCSPs).

Toward better approximation for the minimum-weighted vertex cover (MWVC) problem in multiagent systems, we present a distributed algorithm from the perspective of learning in games. For self-organized coordination and optimization, we see each vertex as a potential game player who makes decisions using local information of its own and the immediate neighbors. The resulting Nash equilibrium is classified into two categories, i.e., the inferior Nash equilibrium (INE) and the dominant Nash equilibrium (DNE). We show that the optimal solution must be a DNE. To achieve better approximation ratios, local rules of perturbation and weighted memory are designed, with the former destroying the stability of an INE and the latter facilitating the refinement of a DNE. By showing the existence of an improvement path from any INE to a DNE, we prove that when the memory length is larger than 1, our algorithm converges in finite time to DNEs, which could not be improved by exchanging the action of a selected node with all its unselected neighbors. Moreover, additional freedom for solution efficiency refinement is provided by increasing the memory length. Finally, intensive comparison experiments demonstrate the superiority of the presented methodology to the state of the art, both in solution efficiency and computation speed.

Distributed constrained optimization for multi-agent networks with nonsmooth objective functions