Trustworthiness Of Agents Research Articles

Improving Agents Trust in Service-Oriented Environment Based on Entropy Structure and Information Ethics Principles

Recently, information ethics has been developed as a new perspective into ethics based on informational and computational considerations. As distrust is an essentially ethical problem in informational environments, it is plausible to think of solutions for trust-related problems in the field of information ethics. This article proposes an entropy-based trust model inspired by entropy structure and information ethics principles as a solution to trust problems in service-oriented environments. The system is capable of maintaining a robust awareness of the trustworthiness of agents. Whereas most trust models are developed as problem-specific solutions, the system proposed here is directly derived from a higher order ethical/informational philosophy that makes it better fit with many problems in an informational environment. The experimental results demonstrate that the performance of the proposed system is promising in terms of trust calculation accuracy and detection of deceptive behavior as well as the adaptation speed to environmental changes.

Trust-based decision-making framework for multiagent system

Multiagent system (MAS) is gaining popular attention in various applications especially with the rapid expansion of internet and computing. In general, MAS considers the collaboration of several agents working in the same environment together to achieve common objectives and also plays a critical role in supporting humans for attaining a comfortable life. Multiagent systems are used in numerous applications such as robotics, cloud computing, e-health, military, etc. Under such circumstances, trust among agents is crucial to ensure successful completion of a task especially when the task could only be completed through sharing of information and resources among the agents. This paper presents for the first time a study on the development of trust estimation models as a part of a decision-making framework that empirically computes and evaluates the trustworthiness of agents in MAS where the agent could choose to cooperate and collaborate only with the other trustworthy agents. The novelty of this study lies in the incorporation of beta reputation system into the Markov Games-based temporal difference learning framework and the development of a novel heuristic computation techniques adapted based on averaging methods. These models function as the integral part of the proposed MAS decision-making framework that chooses agents to collaborate with based on trustworthiness. The developed models and the framework are tested for their accuracy, efficiency and effectiveness using several analyses such as the time steps analysis, root-mean-square analysis and interaction analysis. The performances of the proposed models were better than the performances obtained using the trust models reported in the literature. Further real-world experiments are carried out to test the viability of the developed models in real-world applications.

TrustChain: A Sybil-resistant scalable blockchain

TrustChain is capable of creating trusted transactions among strangers without central control. This enables new areas of blockchain use with a focus on building trust between individuals. Our innovative approach offers scalability, openness and Sybil-resistance while replacing proof-of-work with a mechanism to establish the validity and integrity of transactions.TrustChain is a permission-less tamper-proof data structure for storing transaction records of agents. We create an immutable chain of temporally ordered interactions for each agent. It is inherently parallel and every agent creates his own genesis block. TrustChain includes a novel Sybil-resistant algorithm named NetFlow to determine trustworthiness of agents in an online community. NetFlow ensures that agents who take resources from the community also contribute back. We demonstrate that irrefutable historical transaction records offer security and seamless scalability, without requiring global consensus. Experimentation shows that the transaction throughput of TrustChain surpasses that of traditional blockchain architectures like Bitcoin. We show by using extracted data from a live network that TrustChain has sufficient informativeness to identify freeriders, leading to refusal of service.

Stage: Stereotypical Trust Assessment Through Graph Extraction

Bootstrapping trust assessment where there is little or no evidence regarding a subject is a significant challenge for existing trust and reputation systems. When direct or indirect evidence is absent, existing approaches usually assume that all agents are equally trustworthy. This naive assumption makes existing approaches vulnerable to attacks such as Sybil and whitewashing. Inspired by real‐life scenarios, we argue that malicious agents may share some common patterns or complex features in their descriptions. If such patterns or features can be detected, they can be exploited to bootstrap trust assessments. Based on this idea, we propose the use of frequent subgraph mining and state‐of‐the‐art knowledge representation formalisms to estimate a priori trust for agents. Our approach first discovers significant patterns that may be used to characterize trustworthy and untrustworthy agents. Then, these patterns are used as features to train a regression model to estimate the trustworthiness of agents. Last, a priori trust for unknown agents (e.g., newcomers) is estimated using the discovered features based on the trained model. Through empirical evaluation, we show that the proposed approach significantly outperforms well‐known trust approaches if trustworthiness of agents is correlated with patterns in their descriptions or social networks. Furthermore, we show that the proposed approach performs at least as good as the existing approaches if such correlations do not exist.

An approach to computational social trust

Previous work on computational trust has delivered robust aggregation engines that proved competent in estimating the trustworthiness of agents. However, most of these approaches were evaluated through simulation using simple and static agents' populations that did not consider the social account of trust. In this paper, we show by experimental analysis that these approaches tend to perform poorly when the populations of agents form social structures that evolve with the situation and the relationship existing between partners. Moreover, we present an approach to social-aware computational trust based on Social Tuner, a new component that we developed that infers the benevolence of the trustee toward the truster from the available evidence. We show by experimental analysis that this approach outperforms the social-less aggregation engines in a relevant way. The experiments are run using a model of agents' behavior grounded on the literature of social trust and benevolence that we have developed and describe in detail in this paper.

Data mining agent conversations: A qualitative approach to multiagent systems analysis

This paper presents a novel method for analysing the behaviour of multiagent systems on the basis of the semantically rich information provided by agent communication languages and interaction protocols specified at the knowledge level. More low-level communication mechanisms only allow for a quantitative analysis of the occurrence of message types, the frequency of message sequences, and the empirical distributions of parameter values. Quite differently, the semantics of languages and protocols in multiagent systems can help to extract qualitative properties of observed conversations among agents. This can be achieved by interpreting the logical constraints associated with protocol execution paths or individual messages as the context of an observed interaction, and using them as features of learning samples. The contexts “mined” from such analyses, or context models, can then be used for various tasks, e.g. for predicting others’ future responses (useful when trying to make strategic communication decisions to achieve a particular outcome), to support ontological alignment (by comparing the properties of logical constraints attached to messages across participating agents), or to assess the trustworthiness of agents (by verifying the logical coherence of their behaviour). This paper details a formal approach that describes our notion of context models in multiagent conversations, an implementation of this approach in a practical tool for mining qualitative context models, and experimental results to illustrate its use and utility.

A Probabilistic Approach for Maintaining Trust Based on Evidence

Leading agent-based trust models address two important needs. First, they show how an agent may estimate the trustworthiness of another agent based on prior interactions. Second, they show how agents may share their knowledge in order to cooperatively assess the trustworthiness of others. However, in real-life settings, information relevant to trust is usually obtained piecemeal, not all at once. Unfortunately, the problem of maintaining trust has drawn little attention. Existing approaches handle trust updates in a heuristic, not a principled, manner. This paper builds on a formal model that considers probability and certainty as two dimensions of trust. It proposes a mechanism using which an agent can update the amount of trust it places in other agents on an ongoing basis. This paper shows via simulation that the proposed approach (a) provides accurate estimates of the trustworthiness of agents that change behavior frequently; and (b) captures the dynamic behavior of the agents. This paper includes an evaluation based on a real dataset drawn from Amazon Marketplace, a leading e-commerce site.

Adding security and trust to multiagent systems

Multiagent systems (MASs) are societies whose individuals are software delegatees (agents) acting on behalf of their owners or delegators (people or organizations). When deployed in an open network such as the Internet, MASs face some trust and security issues. Agents comeand go, and interact with strangers. Assumptions about security and general trustworthiness of agents and their deployers are inadequate in this context. In this paper, the design of a security infrastructure is presented applicable to MASs in general. This design addresses both security threats and trust issues. In this design, there are mechanisms for ensuring secure communication among agents and secure naming and resource location services. And two types of trusts are addressed: trust that agents will not misbehave and trust that agents are really delegatees of whom they claim to be. To establish the first type of trust, deployers of agents are made liable for the actions of their agents; to establish the second type of trust, it is proposed that agents prove that they know secrets that only their delegators know.