DISARM: A social distributed agent reputation model based on defeasible logic

Abstract

Intelligent Agents act in open and thus risky environments, hence making the appropriate decision about who to trust in order to interact with, could be a challenging process.As intelligent agents are gradually enriched with Semantic Web technology, acting on behalf of their users with limited or no human intervention, their ability to perform assigned tasks is scrutinized. Hence, trust and reputation models, based on interaction trust or witness reputation, have been proposed, yet they often presuppose the use of a centralized authority. Although such mechanisms are more popular, they are usually faced with skepticism, since users may question the trustworthiness and the robustness of a central authority. Distributed models, on the other hand, are more complex but they provide personalized estimations based on each agent's interests and preferences. To this end, this article proposes DISARM, a novel distributed reputation model. DISARM deals MASs as social networks, enabling agents to establish and maintain relationships, limiting the disadvantages of the common distributed approaches. Additionally, it is based on defeasible logic, modeling the way intelligent agents, like humans, draw reasonable conclusions from incomplete and possibly conflicting (thus inconclusive) information. Finally, we provide an evaluation that illustrates the usability of the proposed model.

Reputation Detection for Information Diffusion in Social Network Systems

“…ere are many representative reputation models, such as Regret [16], FIRE [17], FIRE+ (FIRE+ is an extension of FIRE model proposed by Qureshi et al, which can avoid collusion attacks ) [20], DISARM (DISARM is a social, distributed, hybrid, rulebased reputation model named by Kravari and Bassiliades, which uses defeasible logic) [21], and AFRAS (AFRAS is from "a fuzzy reputation agent system" named by Carbo et al) [22], can be classified as integrated reputation models that generate agents' reputations by integrating many reputation factors.…”

“…e FIRE-+ model [20] is devised to avoid collusion attacks that cannot be avoided by the Fire model; it also mainly focuses on the sources of direct and witness-based interaction experiences, but through a graph construction of witness ratings and various interaction policies (direct interaction policy, witness interaction policy, and connection decision policy), collusion among agents can be effectively detected. DISARM [21] introduces a distributed reputation system considering the relationships among agents as a network; DISARM proposes the use of defeasible logic combining the direct and witness information to support accurate reputation assessment. Wu et al [23] introduce an artificial neural network-based reputation bootstrapping approach, which establishes the reputation of an agent by explicit evidences (direct interaction experiences) and implicit evidences (information that may be relevant with performance) in order to solve the reputation detection problem of newly deployed agents.…”

“…Due to the open nature of SNS, some malicious agents may be involved and engage in malicious behavior that will influence the performance of these systems [4][5][6][7][8]. An efficient way to address this problem makes use of the reputation mechanism, which can provide agents with credits for reference that help them make decisions about whom to trust and encourage agents to engage in trustworthy behavior [5,7,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Collaborative Web Service Discovery and Recommendation Based on Social Link

“…The process employs the best social friendships of the current user and the past invocation histories with satisfactory web services of his friends to generate satisfactory results. To limit the disadvantages of distributed approaches, a novel distributed reputation model was presented in [8], which treats multi-agent systems as social networks and leverages social relations among these agents.…”