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.     

Fuzzy computational models for trust and reputation systems

In the recent past, a considerable research has been devoted to trust and reputation mechanisms to simplify complex transactions for open environments in social networking, e-commerce, and recommender systems (RS). In real life, we come to know about others through our social circle according to their reputation which is a public view. However, it is not always adequate to depend solely on the public view and therefore a trust measure is required to give a personalized view of the future encounters with a specific partner. In this paper, we propose fuzzy computational models for both trust and reputation concepts. Reciprocity and experience are used for trust modeling while the proposed reputation model is a fuzzy extension of beta reputation model. A two-level filtering methodology is proposed to benefit to a large extent from both the concepts separately. In order to justify the proposed models, we compared them with the existing reputation models for movie RS. The experimental results show that the incorporation of trust and reputation concepts into RS indeed improves the recommendation accuracy and establish that our models are better than beta and the popular eBay reputation models.     

Security threats scenarios in trust and reputation models for distributed systems

Trust and reputation management over distributed systems has been proposed in the last few years as a novel and accurate way of dealing with some security deficiencies which are inherent to those environments. Thus, many models and theories have been developed in order to effective and accurately manage trust and reputation in those communities. Nevertheless, very few of them take into consideration all the possible security threats that can compromise the system. In this paper, we present some of the most important and critical security threats that could be applied in a trust and reputation scheme. We will describe and analyze each of those threats and propose some recommendations to face them when developing a new trust and reputation mechanism. We will also study how some trust and reputation models solve them. This work expects to be a reference guide when designing secure trust and reputation models.     

TRIMS,a privacy-aware trust and reputation model for identity management systems

Electronic transactions are becoming more important everyday. Several tasks like buying goods, booking flights or hotel rooms, or paying for streaming a movie, for instance, can be carried out through the Internet. Nevertheless, they are still some drawbacks due to security threats while performing such operations. Trust and reputation management rises as a novel way of solving some of those problems. In this paper we present our work TRIMS (a privacy-aware trust and reputation model for identity management systems), which applies a trust and reputation model to guarantee an acceptable level of security when deciding if a different domain might be considered reliable when receiving certain sensitive user’s attributes. Specifically, we will address the problems which surfaces when a domain needs to decide whether to exchange some information with another possibly unknown domain to effectively provide a service to one of its users. This decision will be determined by the trust deposited in the targeting domain. As far as we know, our proposal is one of the first approaches dealing with trust and reputation management in a multi-domain scenario. Finally, the performed experiments have demonstrated the robustness and accuracy of our model in a wide variety of scenarios.     

A review of attacks and security approaches in open multi-agent systems

Open multi-agent systems (MASs) have growing popularity in the Multi-agent Systems community and are predicted to have many applications in future, as large scale distributed systems become more widespread. A major practical limitation to open MASs is security because the openness of such systems negates many traditional security solutions. In this paper we introduce and classify main attacks on open MASs. We then survey and analyse various security techniques in the literature and categorise them under prevention and detection approaches. Finally, we suggest which security technique is an appropriate countermeasure for which classes of attack.     

Gossip-based aggregation of trust in decentralized reputation systems

Decentralized Reputation Systems have recently emerged as a prominent method of establishing trust among self-interested agents in online environments. A key issue is the efficient aggregation of data in the system; several approaches have been proposed, but they are plagued by major shortcomings. We put forward a novel, decentralized data management scheme grounded in gossip-based algorithms. Rumor mongering is known to possess algorithmic advantages, and indeed, our framework inherits many of their salient features: scalability, robustness, a global perspective, and simplicity. We demonstrate that our scheme motivates agents to maintain a very high reputation, by showing that the higher an agent’s reputation is above the threshold set by its peers, the more transactions it would be able to complete within a certain time unit. We analyze the relation between the amount by which an agent’s average reputation exceeds the threshold and the time required to close a deal. This analysis is carried out both theoretically, and empirically through a simulation system called GossipTrustSim. Finally, we show that our approach is inherently impervious to certain kinds of attacks. A preliminary version of this article appeared in the proceedings of IJCAI 2007.     

Partial identities as a foundation for trust and reputation

This paper explores the relationships between the hard security concepts of identity and privacy on the one hand, and the soft security concepts of trust and reputation on the other hand. We specifically focus on two vulnerabilities that current trust and reputation systems have: the change of identity and multiple identities problems. As a result, we provide a privacy preserving solution to these vulnerabilities which integrates the explored relationships among identity, privacy, trust and reputation. We also provide a prototype of our solution to these vulnerabilities and an application scenario.     

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.     

Towards a trust,reputation and recommendation meta model

New trust, reputation and recommendation (TRR) models are continuously proposed. However, the existing models lack shared bases and goals. For this reason, in this work we define an innovative meta model to facilitate the definition and standardization of a generic TRR model. Following the meta model, researchers in the field will be able to define standard models, compare them with other models and reuse parts of them. A standardization is also needed to determine which properties should be present in a TRR model.In accordance with the objectives we were seeking, following our meta model, we have defined a pre-standardized TRR model for e-commerce, identified the fundamental concepts and the main features that contribute to form trust and reputation in that domain, respected the dependence on the context/role of trust and reputation, aggregated only homogeneous trust information; listed and shown how to defend from the main malicious attacks.     

Balancing the criticisms: Validating multi-agent models of social systems

Using multi-agent models to study social systems has attracted criticisms because of the challenges involved in their validation. Common criticisms that we have encountered are described, and for each one we attempt to give a balanced perspective of the criticism. A model of intra-state conflict is used to help demonstrate these points. We conclude that multi-agent models for social systems are most useful when (1) the connection between micro-behaviors and macro-behaviors are not well-understood and (2) when data collection from the real-world system is prohibitively expensive in terms of time or money or if it puts human lives at risk.     

A comparative study of collaboration-based reputation models for social recommender systems

Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collaboration graphs, from which the reputation of users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recommending relevant pages to users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-user trial of the system.     

Enhancing decentralized service discovery in open service-oriented multi-agent systems

Service-oriented multi-agent systems are dynamic systems that are populated by heterogeneous agents. These agents model their functionality as services in order to allow heterogeneous agents or other entities to interact with each other in a standardized way. Furthermore, due to the large-scale and adaptative needs of the system, traditional directory facilitators or middle-agents are not suitable for the management of agent services. This article proposes the introduction of homophily in service-oriented multi-agent systems to create efficient decentralized and self-organized structures where agents have a greater probability of establishing links with similar agents than with dissimilar ones. This similarity is based on two social dimensions: the set of services that an agent provides and the organizational roles that it plays. A second contribution is an algorithm for service discovery that it is carried out taking into account the local information that is related to the homophily between agents. The experiments compare our proposal with other proposals in distributed environments. The results show that the proposed structure and algorithm offer desirable features for service discovery in decentralized environments. Specifically, these features provide short paths and a high success rate in the service discovery process and resilience under deliberate failures.     

Decentralized tracking-type games for multi-agent systems with coupled ARX models: Asymptotic Nash equilibria

A class of decentralized tracking-type games is considered for large population multi-agent systems (MAS). The agents are described by stochastic discrete-time auto-regressive models with exogenous inputs (ARX models), and coupled together through their individual dynamics and performance indexes by terms of the unknown population state average (PSA). The performance index of each agent to minimize is a stochastic long term averaged group-tracking-type functional, in which there is a nonlinear term of the unknown PSA. The control law is decentralized and implemented via the Nash certainty equivalence principle. By probability limit theory, under mild conditions it is shown that: (a) the estimate of the PSA is strongly consistent; (b) the closed-loop system is stable almost surely, and the stability is independent of the number N of agents; (c) the decentralized control law is an asymptotic Nash equilibrium almost surely or in probability according to the property of the nonlinear coupling function in the performance indexes.     

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.     

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.     

Flocking for multi-agent dynamic systems: algorithms and theory

In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called /spl alpha/-lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or /spl alpha/-agents, and virtual agents associated with /spl alpha/-agents called /spl beta/- and /spl gamma/-agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split/rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms.     

An event-driven high level model for the specification of laws in open multi-agent systems

The agent development paradigm poses many challenges to software engineering researchers, particularly when the systems are distributed and open. They have little or no control over the actions that agents can perform. Laws are restrictions imposed by a control mechanism to deal with uncertainty and to promote open system dependability. In this paper, we present a high level event-driven conceptual model of laws. XMLaw is an alternative approach to specifying laws in open multi-agent systems that presents high level abstractions and a flexible underlying event-based model. Thus XMLaw allows for flexible composition of the elements from its conceptual model and is flexible enough to accept new elements.     

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market's demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature's insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufacturing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets.