TRAMMAS: A tracing model for multiagent systems

Agent's flexibility and autonomy, as well as their capacity to coordinate and cooperate, are some of the features which make multiagent systems useful to work in dynamic and distributed environments. These key features are directly related to the way in which agents communicate and perceive each other, as well as their environment and surrounding conditions. Traditionally, this has been accomplished by means of message exchange or by using blackboard systems. These traditional methods have the advantages of being easy to implement and well supported by multiagent platforms; however, their main disadvantage is that the amount of social knowledge in the system directly depends on every agent actively informing of what it is doing, thinking, perceiving, etc. There are domains, for example those where social knowledge depends on highly distributed pieces of data provided by many different agents, in which such traditional methods can produce a great deal of overhead, hence reducing the scalability, efficiency and flexibility of the multiagent system. This work proposes the use of event tracing in multiagent systems, as an indirect interaction and coordination mechanism to improve the amount and quality of the information that agents can perceive from both their physical and social environment, in order to fulfill their goals more efficiently. In order to do so, this work presents an abstract model of a tracing system and an architectural design of such model, which can be incorporated to a typical multiagent platform.     

A group‐oriented secure multiagent platform

Security is becoming a major concern in multiagent systems, since an agent's incorrect or inappropriate behaviour may cause non‐desired effects, such as money and data loss. Some multiagent platforms (MAP) are now providing baseline security features, such as authentication, authorization, integrity and confidentiality. However, they fail to support other features related to the sociability skills of agents such as agent groups. What is more, none of the listed MAPs provide a mechanism for preserving the privacy of the users (regarding their identities) that run their agents on such MAPs. In this paper, we present the security infrastructure (SI) of the Magentix MAP, which supports agent groups and preserves user identity privacy. The SI is based on identities that are assigned to all the different entities found in Magentix (users, agents and agent groups). We also provide an evaluation of the SI describing an example application built on top of Magentix and a performance evaluation of it. Copyright © 2010 John Wiley & Sons, Ltd.     

Trust management and trust theory revision

A theory of trust for a given system consists of a set of rules that describe trust of agents in the system. In a certain logical framework, the theory is generally established based on the initial trust of agents in the security mechanisms of the system. Such a theory provides a foundation for reasoning about agent beliefs as well as security properties that the system may satisfy. However, trust changes dynamically. When agents lose their trust or gain new trust in a dynamic environment, the theory established based on the initial trust of agents in the system must be revised, otherwise it can no longer be used for any security purpose. This paper investigates the factors influencing trust of agents and discusses how to revise theories of trust in dynamic environments. A methodology for revising and managing theories of trust for multiagent systems is proposed. This methodology includes a method for modeling trust changes, a method for expressing theory changes, and a technique for obtaining a new theory based on a given trust change. The proposed approach is very general and can be applied to obtain an evolving theory of trust for agent-based systems.     

一种基于多智能体进化的广义图染色算法

基于对广义图染色问题的研究,提出了一种求解广义图染色问题的多智能体进化算法(multiagent evolutionary algorithm for T-coloring problem,简称MAEA-TCP),并将该算法应用到实际中的频率分配问题上,取得了良好的效果.该方法中每个智能体作为一个候选解被固定在智能体网格上,为了增加自身能量而与邻域当中的智能体展开竞争或者合作,同时智能体也可以利用自身的知识进行自学习来增加能量.根据广义图染色问题的特点,为智能体设计了3种算子:竞争算子、自学习算子和变异算子,以引导其进化,并用进化的方式来控制各算子,以协调智能体之间的相互作用.在实验中,分别使用大规模的随机图实例和费城实例来测试算法性能,同时给出参数测试结果和最佳取值区间.比较结果表明,该算法优于其他方法,具有良好的收敛性和实用价值.     

Finding influential agent groups in complex multiagent software systems based on citation network analyses

Current complex engineering software systems are often composed of many components and can be built based on a multiagent approach, resulting in what are called complex multiagent software systems. In a complex multiagent software system, various software agents may cite the operation results of others, and the citation relationships among agents form a citation network; therefore, the importance of a software agent in a system can be described by the citations from other software agents. Moreover, the software agents in a system are often divided into various groups, and each group contains the agents undergoing similar tasks or having related functions; thus, it is necessary to find the influential agent group (not only the influential individual agent) that can influence the system outcome utilities more than the others. To solve such a problem, this paper presents a new model for finding influential agent groups based on group centrality analyses in citation networks. In the presented model, a concept of extended group centrality is presented to evaluate the impact of an agent group, which is collectively determined by both direct and indirect citations from other agents outside the group. Moreover, the presented model addresses two typical types of agent groups: one is the adjacent group where agents of a group are adjacent in the citation network, and the other is the scattering group where agents of a group are distributed separately in the citation network. Finally, we present case studies and simulation experiments to prove the effectiveness of the presented model.     

Trust-based security in pervasive computing environments

Traditionally, stand-alone computers and small networks rely on user authentication and access control to provide security. These physical methods use system-based controls to verify the identity of a person or process, explicitly enabling or restricting the ability to use, change, or view a computer resource. However, these strategies are inadequate for the increased flexibility that distributed networks such as the Internet and pervasive computing environments require because such systems lack central control and their users are not all predetermined. Mobile users expect to access locally hosted resources and services anytime and anywhere, leading to serious security risks and access control problems. We propose a solution based on trust management that involves developing a security policy, assigning credentials to entities, verifying that the credentials fulfill the policy, delegating trust to third parties, and reasoning about users' access rights. This architecture is generally applicable to distributed systems but geared toward pervasive computing environments     

Zeus: A toolkit for building distributed multiagent systems

The multiagent systems approach of knowledge- level cooperation between autonomous agents promises significant benefits to distributed systems engineering, such as enhanced interoperability, scalability, and reconfigurability. However, thus far, because of the innate difficulty of constructing multiagent systems, this promise has been largely unrealized. Hence there is an emerging desire among agent developers to move away from developing point solutions to point problems in favor of developing methodologies and toolkits for building distributed multiagent systems. This philosophy led to the development of the ZEUS Agent Building Toolkit, which facilitates the rapid development of collaborative agent applications through the provision of a library of agent- level components and an environment to support the agent-building process. The ZEUS toolkit is a synthesis of established agent technologies with some novel solutions to provide an integrated collaborative agent-building environment.     

A capabilities-based model for adaptive organizations

Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environments make multiagent systems susceptible to individual failures that can significantly reduce its ability to accomplish its overall goal. The problem is that multiagent systems are typically designed to work within a limited set of configurations. Even when the system possesses the resources and computational power to accomplish its goal, it may be constrained by its own structure and knowledge of its member’s capabilities. To overcome these problems, we are developing a framework that allows the system to design its own organization at runtime. This paper presents a key component of that framework, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems. This model defines the requisite knowledge of a system’s organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent’s capabilities.     

Decentralized MDPs with sparse interactions

Creating coordinated multiagent policies in environments with uncertainty is a challenging problem, which can be greatly simplified if the coordination needs are known to be limited to specific parts of the state space. In this work, we explore how such local interactions can simplify coordination in multiagent systems. We focus on problems in which the interaction between the agents is sparse and contribute a new decision-theoretic model for decentralized sparse-interaction multiagent systems, Dec-SIMDPs, that explicitly distinguishes the situations in which the agents in the team must coordinate from those in which they can act independently. We relate our new model to other existing models such as MMDPs and Dec-MDPs. We then propose a solution method that takes advantage of the particular structure of Dec-SIMDPs and provide theoretical error bounds on the quality of the obtained solution. Finally, we show a reinforcement learning algorithm in which independent agents learn both individual policies and when and how to coordinate. We illustrate the application of the algorithms throughout the paper in several multiagent navigation scenarios.     

Environment as a first class abstraction in multiagent systems

The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems. In this paper, we put forward the environment as an explicit part of multiagent systems.We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for environment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.     

The emergence of controllable transient behavior using an agent diversification strategy

The main subject of this study is to inquire whether it is possible to control transient dynamics in a multiagent dynamical system. It is a well known fact that any multiagent dynamical system follows its transient dynamics, whether its transient dynamics are temporal or not, whenever a dynamical system starts operating at any initial condition. Especially in the case of a transient state occurring frequently due to intermittent perturbations, such as from user's requests, most tasks would be processed on the system's transient dynamics. Then undoubtedly you would care to ask "Is it possible to control transient dynamics on performance and life span, or not?" So far however, for transient dynamics being rated as temporal and uncontrollable dynamics, any systematic knowledge on controlling transient dynamics for multiagent dynamical systems has not been clarified. This study has introduced a new type of agent strategy called "info-reliability." It has clarified the diversification of agent strategy as an available procedure to make the system's transient dynamics controllable on its performance and on the life span of the transient dynamics.     

Biotope: an integrated framework for simulating distributed multiagent computational systems

The study of distributed computational systems issues, such as heterogeneity, concurrency, control, and coordination, has yielded a number of models and architectures, which aspire to provide satisfying solutions to each of the above problems. One of the most intriguing and complex classes of distributed systems are computational ecosystems, which add an "ecological" perspective to these issues and introduce the characteristic of self-organization. Extending previous research work on self-organizing communities, we have developed Biotope, which is an agent simulation framework, where each one of its members is dynamic and self-maintaining. The system provides a highly configurable interface for modeling various environments as well as the "living" or computational entities that reside in them, while it introduces a series of tools for monitoring system evolution. Classifier systems and genetic algorithms have been employed for agent learning, while the dispersal distance theory has been adopted for agent replication. The framework has been used for the development of a characteristic demonstrator, where Biotope agents are engaged in well-known vital activities-nutrition, communication, growth, death-directed toward their own self-replication, just like in natural environments. This paper presents an analytical overview of the work conducted and concludes with a methodology for simulating distributed multiagent computational systems.     

Using bayesian networks to model agent relationships

An agent-society of the future is envisioned to be as complex as a human society. Just like human societies, such multiagent systems (MAS) deserve an in-depth study of the dynamics, relationships, and interactions of the constituent agents. An agent in a MAS may have only approximate a priori estimates of the trustworthiness of another agent. But it can learn from interactions with other agents, resulting in more accurate models of these agents and their dependencies together with the influences of other environmental factors. Such models are proposed to be represented as Bayesian or belief networks. An objective mechanism is presented to enable an agent elicit crucial information from the environment regarding the true nature of the other agents. This mechanism allows the modeling agent to choose actions that will produce guaranteed minimal improvement of the model accuracy. The working of the proposed maxim in entropy procedure is demonstrated in a multiagent scenario.     

Design and implementation of a multiagent stock trading system

Stock trading is one of the key items in an economy and estimating its behavior and taking the best decision in it are among the most challenging issues. Solutions based on intelligent agent systems are proposed to cope with those challenges. Agents in a multiagent system (MAS) can share a common goal or they can pursue their own interests. That nature of MASs exactly fits the requirements of a free market economy. Although existing studies include noteworthy proposals on agent‐based market simulation and researchers discuss theoretical design issues of agent‐based stock exchange systems, unfortunately only a very few of the studies consider exact development and implementation of multiagent stock trading systems within the software engineering perspective and guides to the software engineers for constructing such software systems starting from scratch. To fill this gap, in this paper, we discuss the development of a multiagent‐based stock trading system by taking into consideration software design according to a well‐defined agent oriented software engineering methodology and implementation with a widely‐used MAS software development framework. Each participant in the system is first designed as belief–desire–intention agents with their facts, goals, and plans, and then belief–desire–intention reasoning and behavioral structure of the designed agents are implemented. Lessons learned during design and development within the software engineering perspective and evaluation of the implemented multiagent stock exchange system are also reported. Copyright © 2011 John Wiley & Sons, Ltd.     

Locality-sensitive task allocation and load balancing in networked multiagent systems: Talent versus centrality

With the development of large scale multiagent systems, agents are always organized in network structures where each agent interacts only with its immediate neighbors in the network. Coordination among networked agents is a critical issue which mainly includes two aspects: task allocation and load balancing; in traditional approach, the resources of agents are crucial to their abilities to get tasks, which is called talent-based allocation. However, in networked multiagent systems, the tasks may spend so much communication costs among agents that are sensitive to the agent localities; thus this paper presents a novel idea for task allocation and load balancing in networked multiagent systems, which takes into account both the talents and centralities of agents. This paper first investigates the comparison between talent-based task allocation and centrality-based one; then, it explores the load balancing of such two approaches in task allocation. The experiment results show that the centrality-based method can reduce the communication costs for single task more effectively than the talent-based one, but the talent-based method can generally obtain better load balancing performance for parallel tasks than the centrality-based one.     

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.     

多Agent系统的研究

自agent术语引起国内学者重视以来,人们一直试图寻找一个贴切的汉语名词,“代理人”“智能体洲结点”等概念应运而生。但这些概念都不能令人满意,因为无论是“智能体”、“代理人”,还是“结点”,都仅部分地反映了agent的特征,而未完全地反映其本质属性,故本文仍使用agent的英文形式。     

Continual planning and acting in dynamic multiagent environments

In order to behave intelligently, artificial agents must be able to deliberatively plan their future actions. Unfortunately, realistic agent environments are usually highly dynamic and only partially observable, which makes planning computationally hard. For most practical purposes this rules out planning techniques that account for all possible contingencies in the planning process. However, many agent environments permit an alternative approach, namely continual planning, i.e. the interleaving of planning with acting and sensing. This paper presents a new principled approach to continual planning that describes why and when an agent should switch between planning and acting. The resulting continual planning algorithm enables agents to deliberately postpone parts of their planning process and instead actively gather missing information that is relevant for the later refinement of the plan. To this end, the algorithm explictly reasons about the knowledge (or lack thereof) of an agent and its sensory capabilities. These concepts are modelled in the planning language (MAPL). Since in many environments the major reason for dynamism is the behaviour of other agents, MAPL can also model multiagent environments, common knowledge among agents, and communicative actions between them. For Continual Planning, MAPL introduces the concept of of assertions, abstract actions that substitute yet unformed subplans. To evaluate our continual planning approach empirically we have developed MAPSIM, a simulation environment that automatically builds multiagent simulations from formal MAPL domains. Thus, agents can not only plan, but also execute their plans, perceive their environment, and interact with each other. Our experiments show that, using continual planning techniques, deliberate action planning can be used efficiently even in complex multiagent environments.     

Market micro-structure analysis by multiagent simulation in X-Economy -- comparison among technical indices

We are developing an agent and server library referred to as X-Economy, by which we can execute multiagent simulations and network games for financial and economic systems. To this end, we analyzed the characteristics of network games in a financial context and compared them with traditional ones. X-Economy has also provided a new research direction in market micro-structure analysis. We executed several kinds of multiagent simulations for technical traders (indices) and obtained non-trivial suggestions regarding the relationship between the market randomness and the effectiveness of technical indices. For instance, the performance of complex technical indices seemed to deeply depend on the characteristics and nature of a market when a market became complex, i.e. it moved far from the Wiener process.