Query join ordering optimization with evolutionary multi-agent systems

This work presents an evolutionary multi-agent system applied to the query optimization phase of Relational Database Management Systems (RDBMS) in a non-distributed environment. The query optimization phase deals with a known problem called query join ordering, which has a direct impact on the performance of such systems. The proposed optimizer was programmed in the optimization core of the H2 Database Engine. The experimental section was designed according to a factorial design of fixed effects and the analysis based on the Permutations Test for an Analysis of Variance Design. The evaluation methodology is based on synthetic benchmarks and the tests are divided into three different experiments: calibration of the algorithm, validation with an exhaustive method and a general comparison with different database systems, namely Apache Derby, HSQLDB and PostgreSQL. The results show that the proposed evolutionary multi-agent system was able to generate solutions associated with lower cost plans and faster execution times in the majority of the cases.     

An individualistic approach to social action in multi-agent systems

In this paper, we propose an individualistic formal model of coordination and social action in Multi-Agent Systems (MAS). In the first stage, the agents recognize potential for cooperation by reasoning about how they depend on each other. In the second phase, the agents negotiate directly: Following ‘social’ strategies, the agents propose, accept or refuse different offers until they reach a ‘fair’ agreement. The process itself is seen as an exchange of commissive speech acts, through which the agents adopt social commitments. Finally, joint commitments and societies are defined in individual terms. Joint plans are seen as deals and team activity as a special case of social activity. Moreover, agents are allowed to relax their initial goals and negotiate over compatible subgoals. They will cooperate as long as they have common interests. This approach improves previous models of coordination in three ways: (a) it is simpler, because team and plan formation are considered in the same stage; (b) it is clearer, because there are not representational gaps along the coordination process; (c) it is more comprehensive, because it is applicable to MAS in general, not only to Cooperative Problem Solving (CPS).     

Leader localization in multi-agent systems subject to failure: A graph-theoretic approach

In this paper, structural controllability of a leader–follower multi-agent system with multiple leaders is studied. A graphical condition for structural controllability based on the information flow graph of the system is provided. The notions of p-link and q-agent controllability in a multi-leader setting are then introduced, which provide quantitative measures for the controllability of a system subject to failure in the agents and communication links. The problem of leader localization is introduced, which is concerned with finding the minimum number of agents whose selection as leaders results in a p-link or q-agent controllable network. Polynomial-time algorithms are subsequently presented to solve the problem for both cases of undirected and directed information flow graphs.     

Global robust distributed output consensus of multi-agent nonlinear systems: An internal model approach

This paper studies the problem of global robust distributed output consensus of heterogeneous leader–follower multi-agent nonlinear systems by general directed output interactions. For a class of minimum-phase single-input single-output nonlinear agents having unity relative degree, it is shown that the problem is solvable by an internal model approach under certain mild conditions. A Lyapunov function based output-feedback control law is developed by converting the global output consensus into a global distributed stabilization problem for an augmented network.     

Toward a new aspect-mining approach for multi-agent systems

Many aspect mining techniques have been proposed for object-oriented systems. Unfortunately, aspect mining for multi-agent systems is an unexplored research area. The inherent specificities of multi-agent systems (such as autonomy, pro-activity, reactivity, and adaptability) make it difficult to understand, reuse and maintain their code. We propose, in this paper, a (semi-automatic) hybrid aspect mining approach for agent-oriented code. The technique is based on both static and dynamic analyzes. The main motivations of this work are (1) identifying cross-cutting concerns in existing agent-oriented code, and (2) making them explicitly available to software engineers involved in the evolution of agent-oriented code in order to facilitate its refactoring and, consequently, to improve its understandability, reusability and maintainability. The proposed approach is supported by a software tool, called MAMIT (MAS Aspect-MIning Tool), that we developed. The approach and the associated tool are illustrated using a concrete case study.     

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.     

An abstract knowledge based approach to diagnosis and recovery of plan failure in multi-agent systems

Open multi-agent systems (MAS) are decentralised and distributed systems that consist of a large number of loosely coupled autonomous agents. In the absence of centralised control they tend to be difficult to manage, especially in an open environment, which is dynamic, complex, distributed and unpredictable. This dynamism and uncertainty in an open environment gives rise to unexpected plan failures. In this paper we present an abstract knowledge based approach for the diagnosis and recovery of plan action failures. Our approach associates a sentinel agent with each problem solving agent in order to monitor the problem solving agent’s interactions. The proposed approach also requires the problem solving agents to be able to report on the status of a plan’s actions.Once an exception is detected the sentinel agents start an investigation of the suspected agents. The sentinel agents collect information about the status of failed plan abstract actions and knowledge about agents’ mental attitudes regarding any failed plan. The sentinel agent then uses this abstract knowledge and the agents’ mental attitudes, to diagnose the underlying cause of the plan failure. The sentinel agent may ask the problem solving agent to retry their failed plan based on the diagnostic result.     

Applications and environments for multi-agent systems

This paper addresses multi agent system (MAS) environments from an application perspective. It presents a structured view on environment-centric MAS applications. This comprises three base configurations, which MAS applications may apply directly or combine into a composite configuration. For each configuration, the paper presents key issues, requirements and opportunities (e.g. time management issues, real-world augmentation opportunities and state snapshot requirements). Thus, the paper delineates what environment technology may implement to serve MAS applications. Sample applications illustrate the configurations. Next, electronic institutions provide an example of an environment technology, addressing norms and laws in an agent society, already achieving some maturity. In comparison, application-domain specific environment technologies still are in embryonic stages.     

分布式优化的多智能体方法

分布式优化作为分布式协调控制领域中的一个基本而重要的研究课题,近年来,不同领域的众多学者对其产生了广泛的研究兴趣.本文总结归纳了分布式优化的研究现状和近期的研究成果,重点对离线分布式优化和在线分布式优化进行了阐述,并从算法设计和收敛性分析这两个角度进行了剖析.特别地,针对一类混合均衡问题,本文介绍了一类分布式求解算法.最后,阐述了当前尚未解决的问题和未来的研究方向.     

An evolutionary approach to the delineation of functional areas based on travel-to-work flows

This paper presents a new approach to the delineation of local labor markets based on evolutionary computation. The aim of the exercise is the division of a given territory into functional regions based on travel-to-work flows. Such regions are defined so that a high degree of inter-regional separation and of intra-regional integration in both cases in terms of commuting flows is guaranteed. Additional requirements include the absence of overlap between delineated regions and the exhaustive coverage of the whole territory. The procedure is based on the maximization of a fitness function that measures aggregate intra-region interaction under constraints of inter-region separation and minimum size. In the experimentation stage, two variations of the fitness function are used, and the process is also applied as a final stage for the optimization of the results from one of the most successful existing methods, which are used by the British authorities for the delineation of travel-to-work areas （TTWAs）. The empirical exercise is conducted using real data for a sufficiently large territory that is considered to be representative given the density and variety of travel-to-work patterns that it embraces. The paper includes the quantitative comparison with alternative traditional methods, the assessment of the performance of the set of operators which has been specifically designed to handle the regionalization problem and the evaluation of the convergence process. The robustness of the solutions, something crucial in a research and policy-making context, is also discussed in the paper.     

Swarming behavior of multi—agent systems

We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties. It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite size around the swarm center in a finite time. Moreover, we extend our results to more general attraction/repttlsion functions. Numerical simulations demonstrate that all agents will eventually enter into and remain in a bounded region around the swarm center which may exhibit complex spiral motion due to asymmetry of the coupling structure. The model in this paper is more general than isotropic swarms and our results provide fiarther insight into the effect of the interaction pattem on individual motion in a swarm system.     

A multi-agent architecture for control of AGV systems

Agent is an autonomous, computational entity that can be viewed as perceiving its environment and acting upon it. Agents are event-driven objects that can be integrated in automated manufacturing environments to control certain tasks. In this paper a set of agents (a multi-agent system) is introduced to control an automated manufacturing environment. The architecture includes functions at the manufacturing cell level, materials handling and transport level, and factory scheduling level. Communication between these agents is accomplished by using a relational database (blackboard system). The relational database also integrates the requirements of a manufacturing execution system within the multi-agent task structure, which is unique to this architecture. Manufacturing cell and scheduling agents have been previously described in the literature. Here we focus our attention on the functions of the agents of the transport system, which is composed of a set of AGVs.     

Cell size determination in WCDMA systems using an evolutionary programming approach

This paper deals with the problem of the cell size determination in WCDMA-based mobile networks, in multiservice environments. The objective is to obtain the maximum cell size, given a set of services with their corresponding constraints, in terms of quality of service (QoS), binary rate, etc. To achieve this, we have to find the optimal services’ load factors which maximizes the cell radius of the system under traffic criteria. We apply an evolutionary programming algorithm to solve the problem, which codifies and evolves the services’ load factors. We have compared our approach with an existing algorithm in several multiservice scenarios, improving its solutions in terms of cell size.     

Game theoretical applications for multi-agent systems

We consider game-theoretic principles for design of cooperative and competitive (non-cooperative self-interested) multi-agent systems. Using economic concepts of tâtonnement and economic core, we show that cooperative multi-agent systems should be designed in games with dominant strategies that may lead to social dilemmas. Non-cooperative multi-agent systems, on the other hand, should be designed for games with no clear dominant strategies and high degree of problem complexity. Further, for non-cooperative multi-agent systems, the number of learning agents should be carefully managed so that solutions in the economic core can be obtained. We provide experimental results for the design of cooperative and non-cooperative MAS from telecommunication and manufacturing industries.     

Cooperative state estimation of multi-agent systems subject to bounded external disturbances

This paper deals with the problem of cooperative state estimation of general linear multi-agent systems subject to heterogeneous bounded external disturbances. This problem specifies the objective that each agent estimates its own state by using only relative information from its neighbours. Because of the existence of external disturbances, the problem is challenging especially when stringent exact estimation performance is desired. To this end, two cooperative estimation protocols are proposed, including the discontinuous nonlinear protocol for exact state estimation and the continuous nonlinear protocol for estimation with bounded errors. The overall network stability and convergence properties are analysed using the Lyapunov function method. A simulation example has also been used to demonstrate the effectiveness of the proposed results.     

Exception representation and management in open multi-agent systems

One of the major issues in dealing with exceptions in open multi-agent systems (MAS) is lack of uniform representation of exceptions and their shared semantics. In the absence of a uniform framework different business organizations may use different representations for the same exception or may interpret the same exception in different ways. In order to address this issue we apply an ontological approach as a uniform way of representing and interpreting exceptions in cross-organizational settings. This helps agents from different organizations interpret exceptional situations in an unambiguous way and exchange exception related information using standard structures. We believe that an exception ontology along with a domain ontology increases the open MAS reliability and also enhances its fault tolerance capability. The proposed ontology is built using the ontological support provided by the JADE agent framework and exception diagnoses agents are implemented using the JACK™ agent framework.     

An evolutionary approach for multi-objective vehicle routing problems with backhauls

The vehicle routing problem (VRP) is an important aspect of transportation logistics with many variants. This paper studies the VRP with backhauls (VRPB) in which the set of customers is partitioned into two subsets: linehaul customers requiring a quantity of product to be delivered, and backhaul customers with a quantity to be picked up. The basic VRPB involves finding a collection of routes with minimum cost, such that all linehaul and backhaul customers are serviced. A common variant is the VRP with selective backhauls (VRPSB), where the collection from backhaul customers is optional. For most real world applications, the number of vehicles, the total travel cost, and the uncollected backhauls are all important objectives to be minimized, so the VRPB needs to be tackled as a multi-objective problem. In this paper, a similarity-based selection evolutionary algorithm approach is proposed for finding improved multi-objective solutions for VRPB, VRPSB, and two further generalizations of them, with fully multi-objective performance evaluation.     

Event-based consensus of multi-agent systems with general linear models

In this paper, the event-based consensus problem of general linear multi-agent systems is considered. Two sufficient conditions with or without continuous communication between neighboring agents are presented to guarantee the consensus. The advantage of the event-based strategy is the significant decrease of the number of controller updates for cooperative tasks of multi-agent systems involving embedded microprocessors with limited on-board resources. The controller updates of each agent are driven by properly defined events, which depend on the measurement error, the states of its neighboring agents and an arbitrarily small threshold. It is shown that the controller updates for each agent only trigger at its own event time instants. A simulation example is presented to illustrate the theoretical results.