Coordination in multiagent systems and Laplacian spectra of digraphs

Constructing and studying distributed control systems requires the analysis of the Laplacian spectra and the forest structure of directed graphs. In this paper, we present some basic results of this analysis. We also discuss the application of these results published earlier to decentralized control and touch upon some problems of spectral graph theory.     

Declarative representations of multiagent systems

This paper explores the specification and semantics of multiagent problem-solving systems, focusing on the representations that agents have of each other. It provides a declarative representation for such systems. Several procedural solutions to a well-known test-bed problem are considered, and the requirements they impose on different agents are identified. A study of these requirements yields a representational scheme based on temporal logic for specifying the acting, perceiving, communicating, and reasoning abilities of computational agents. A formal semantics is provided for this scheme. The resulting representation is highly declarative, and useful for describing systems of agents solving problems reactively     

Controllability of heterogeneous multiagent systems

The existing results on controllability of multiagent systems (MASs) are mostly based on homogeneous nodes. This paper focuses on controllability of heterogeneous MASs, where the agents are modeled as two types. One type is that the agents have the same high‐order dynamics, and the interconnection topologies of the information flow in different orders are supposed to be different; the other type is that the agents have generic linear dynamics, and the dynamics are supposed to be heterogeneous. For the first type, the necessary and sufficient condition for controllability of heterogeneous‐topology system is derived via combination of Laplacian matrices. For the second type, the contribution also has two parts. The first part supposes that the agents have the same dimensional states and proves that controllability of this kind of MASs is equivalent to the controllability of each node and the whole interconnection topology, while the last parameter of the state feedback vector must not be 0. The second part supposes that the agents may have different dimensional states. For this kind of systems, the concept of β‐controllability is proposed. The necessary and sufficient condition for β‐controllability of heterogeneous‐dynamic systems is also derived and it is also proved that the feedback gain vectors have the effect to improve controllability. Different illustrative examples are provided to demonstrate the effectiveness of the theoretical results in this paper.     

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

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.     

分布式网络系统的任意预设时间分组一致

研究任意预设时间控制下的多智能体网络系统分组一致性问题.设计非零分组投影参数下任意预设时间控制协议,使得分布式网络系统在物理允许范围内的任意预设时间内迅速实现分组一致,该预设时间与系统参数和初始值都无关系.基于代数图论、李雅普诺夫稳定性和矩阵理论等,分别讨论无向和有向拓扑网络情形下,多智能体系统实现预设时间分组一致的充分条件.独轮车的多智能体系统仿真实验验证了所提方法的有效性.     

Nonlinear prediction of manufacturing systems through explicit and implicit data mining

Many processes in the industrial realm exhibit stochastic and nonlinear behavior. Consequently, an intelligent system must be able to ndapt to nonlinear production processes as well as probabilistic phenomena. To this end, an intelligent manufacturing system may draw on techniques from disparate fields, involving knowledge in both explicit and implicit form.In order for a knowledge based system to control a manufacturing process, an important capability is that of prediction: forecasting the future trajectory of a process as well as the consequences of the control action. This paper presents a comparative study of explicitaand implicit methods to predict nonlinear chaotic behavior. The evaluated models include statistica; procedures as well as neural networks and case based reasoning. The concepts are crystallized through a case study in the prediction of chaotic processes adulterated by various patterns of noise.     

Modelling social norms in multiagent systems

Social norms are cultural phenomena that naturally emerge in human societies and help to prescribe and proscribe normative patterns of behaviour. In recent times, the discipline of multi-agent systems has been used to model social norms in an artificial society of agents. In this paper we review norms in multi-agent systems and then explore a series of norms in a simulated urban traffic setting. Using game-theoretic concepts we define and offer an account of norm stability. Particularly in small groups, a relatively small number of individuals with cooperative attitude are needed for the norm of cooperation to evolve and be stable. In contrast, in larger populations, a larger proportion of cooperating individuals are required to achieve stability.     

Communication in reactive multiagent robotic systems

Multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. Communication between the robots can multiply their capabilities and effectiveness, but to what extent? In this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. Performance was measured for three different types of communication for three different tasks. The levels of communication are progressively more complex and potentially more expensive to implement. For some tasks, communication can significantly improve performance, but for others inter-agent communication is apparently unnecessary. In cases where communication helps, the lowest level of communication is almost as effective as the more complex type. The bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. The simulation results help determine appropriate parameters for the reactive control system which was ported for tests on Denning mobile robots.     

A platform-independent metamodel for multiagent systems

Various agent-oriented methodologies and metamodels exist to design and develop multiagent systems (MAS) in an abstract manner. Frequently, these frameworks specialise on particular parts of the MAS and only few works have been invested to derive a common standardisation. This limits the impact of agent-related systems in commercial applications. In this paper, we present a metamodel for agent systems that abstracts from existing agent-oriented methodologies, programming languages, and platforms and could thus be considered as platform-independent. This metamodel defines the abstract syntax of a proposed domain-specific modelling language for MAS that is currently under development and provides furthermore the base to generate code out of the generated designs. This is done by applying the principles of model-driven development (MDD) and providing two model transformations that allow transforming the generated models into textual code that can be executed with JACK and JADE.     

Research directions for service-oriented multiagent systems

Today's service-oriented systems realize many ideas from the research conducted a decade or so ago in multiagent systems. Because these two fields are so deeply connected, further advances in multiagent systems could feed into tomorrow's successful service-oriented computing approaches. This article describes a 15-year roadmap for service-oriented multiagent system research.     

Integrating trust measures in multiagent systems

Several models have been proposed in the past for representing both reliability and reputation. However, we remark that a crucial point in the practical use of these two measures is represented by the possibility of suitably combining them to support the agent's decision. In the past, we proposed a reliability–reputation model, called RRAF, that allows the user to choose how much importance to give to the reliability with respect to the reputation. However, RRAF shows some limitations, namely: (i) The weight to assign to the reliability versus reputation is arbitrarily set by the user, without considering the system evolution; (ii) the trust measure that an agent a perceives about an agent b is completely independent of the trust measure perceived by each other agent c, while in the reality the trust measures are mutually dependent. In this paper, we propose an extension of RRAF, aiming at facing the limitations above. In particular, we introduce a new trust reputation model, called TRR, that considers, from a mathematical viewpoint, the interdependence among all the trust measures computed in the systems. Moreover, this model dynamically computes a parameter measuring the importance of the reliability with respect to the reputation. Some experiments performed on the well‐known ART(Agent Reputation and Trust) platform show the significant advantages in terms of effectiveness introduced by TRR with respect to RRAF. © 2011 Wiley Periodicals, Inc.     

Strategic best-response learning in multiagent systems

We present a novel and uniform formulation of the problem of reinforcement learning against bounded memory adaptive adversaries in repeated games, and the methodologies to accomplish learning in this novel framework. First we delineate a novel strategic definition of best response that optimises rewards over multiple steps, as opposed to the notion of tactical best response in game theory. We show that the problem of learning a strategic best response reduces to that of learning an optimal policy in a Markov Decision Process (MDP). We deal with both finite and infinite horizon versions of this problem. We adapt an existing Monte Carlo based algorithm for learning optimal policies in such MDPs over finite horizon, in polynomial time. We show that this new efficient algorithm can obtain higher average rewards than a previously known efficient algorithm against some opponents in the contract game. Though this improvement comes at the cost of increased domain knowledge, simple experiments in the Prisoner's Dilemma, and coordination games show that even when no extra domain knowledge (besides that an upper bound on the opponent's memory size is known) is assumed, the error can still be small. We also experiment with a general infinite-horizon learner (using function-approximation to tackle the complexity of history space) against a greedy bounded memory opponent and show that while it can create and exploit opportunities of mutual cooperation in the Prisoner's Dilemma game, it is cautious enough to ensure minimax payoffs in the Rock–Scissors–Paper game.     

Visualization and debugging of distributed multiagent systems

Visualizing the behavior of systems with distributed data, control, and process is a notoriously difficult task. Each component in the distributed system has only a local view of the whole setup, and the onus is on the user to integrate, into a coherent whole, the large amounts of limited information they provide. In this article, we describe an architecture and an implemented system for visualizing and controlling distributed multiagent applications. The system comprises a suite of tools, with each tool providing a different perspective of the application being visualized . Each tool interrogates the components of the distributed application, collates the returned information, and presents this information to users in an appropriate manner. This in essence, shifts the burden ofinference from the user to the visualizer. Our visualizer has been evaluated on four distributed multiagent systems: a travel management application, a telecommunications network management application, a business process management demonstrator, and an electronic commerce application. Lastly, we briefly show how the suite of tools can be used together for debugging multiagent applications - a process we refer to as debugging via corroboration.     

Infrastructures for the environment of multiagent systems

The notion of environment is receiving an increasing attention in the development of multiagent applications. This is witnessed by the emergence of a number of infrastructures providing agent designers with useful means to develop the agent environment, and thus to structure an effective multiagent application. In this paper we analyse the role and features of such infrastructures, and survey some relevant examples. We endorse a general viewpoint where the environment of a multiagent system is seen as a set of basic bricks we call environment abstractions, which (i) provide agents with services useful for achieving individual and social goals, and (ii) are supported by some underlying software infrastructure managing their creation and exploitation. Accordingly, we focus the survey on the opportunities that environment infrastructures provide to system designers when developing multiagent applications.     

Explaining interdependent action delays in multiagent plans execution

In this paper we address the problem of diagnosing the execution of Multiagent Plans with interdependent action delays. To this end, we map our problem to the Model-Based Diagnosis setting, and solve it by devising a novel modeling and reasoning method to infer preferred diagnoses based on partial observation of the start and end times of plan actions. Interestingly, we show that the kind of problem we address can be seen as an extension to the well known disjunctive temporal problem with preferences, augmented with a (qualitative) Bayesian network that models dependencies among action delays. An extensive set of tests performed with a prototype implementation on two different problem domains proves the feasibility of the proposed methodology.     

An authorization-based trust model for multiagent systems

In this paper an authorization-based trust model (ABTM) is described which is designed for managing access to services in a semi-open distributed environment. This is called a multiagent-based smart office environment. In this model, "trust" is defined as a set of authorization attributes that are granted by the owner of a service to the user of the service. Central to this model is a trust manager that redelegates authorizations from the service owner to the requesting user, based on access control policies that are specified by role labels which are assigned to a set of agents. The ABTM scheme is different from a centralized scheme, in which authorizations are granted directly by an authority. It is also different from a fully distributed system,where authorizations are granted based solely on the discretion of the owner of the services. The design philosophy is the separation of trust management and trust application to allow efficient management of access control in large-scale and dynamic environment, such as those that exist in multiagent systems.     

Practical multiagent rendezvous through modified circumcenter algorithms

We present a class of modified circumcenter algorithms that allow a group of agents to achieve “practical rendezvous” when they are only able to take noisy measurements of their neighbors. Assuming a uniform detection probability in a disk of radius σ about each neighbor’s true position, we show how initially connected agents converge to a practical stability ball. More precisely, a deterministic analysis allows us to guarantee convergence to such a ball under r-disk graph connectivity in 1D under the condition that r/σ be sufficiently large. A stochastic analysis leads to a similar convergence result in probability, but for any r/σ>1, and under a sequence of switching graphs that contains a connected graph within bounded time intervals. We include several simulations to discuss the performance of the proposed algorithms.     

Coordinating patient preferences through automated negotiation: A multiagent systems model for diagnostic services scheduling

This paper presents a multiagent systems model for patient diagnostic services scheduling. We assume a decentralized environment in which patients are modeled as self-interested agents who behave strategically to advance their own benefits rather than the system wide performance. The objective is to improve the utilization of diagnostic imaging resources by coordinating patient individual preferences through automated negotiation. The negotiation process consists of two stages, namely patient selection and preference scheduling. The contract-net protocol and simulated annealing based meta-heuristics are used to design negotiation protocols at the two stages respectively. In terms of game theoretic properties, we show that the proposed protocols are individually rational and incentive compatible. The performance of the preference scheduling protocol is evaluated by a computational study. The average percentage gap analysis of various configurations of the protocol shows that the results obtained from the protocol are close to the optimal ones. In addition, we present the algorithmic properties of the preference scheduling protocol through the validation of a set of eight hypotheses.     

On consensus protocols for high-order multiagent systems

In this paper, the static consensus problem and the dynamic consensus problem are considered for a class of high-order multiagent systems. With the proposed consensus protocols, necessary and sufficient conditions for the consensus problems are obtained. For the static consensus protocol, the desired consensus speed can be achieved by adjusting feedback gains. Simulations show the effectiveness of the proposed consensus protocols.