68,083 research outputs found

    Agent based computational model of trust

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    This paper employs the methodology of Agent-Based Computational Economics (ACE) to investigate under what conditions trust can be viable in markets. The emergence and breakdown of trust is modeled in a context of multiple buyers and suppliers. Agents adapt their trust in a partner, the weight they attach to trust relative to profitability, and their own trustworthiness, modeled as a threshold of defection. Adaptation occurs on the basis of realized profit. Trust turns out to be viable under fairly general conditions

    A computation trust model with trust network in multi-agent systems

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    Trust is a fundamental issue in multi-agent systems, especially when they are applied in e-commence. The computational models of trust play an important role in determining who and how to interact in open and dynamic environments. To this end, a computation trust model is proposed in which the confidence information based on direct prior interactions with the target agent and the reputation information from trust network are used. In this way, agents can autonomously deal with deception and identify trustworthy parties in multi-agent systems. The ontological property of trust is also considered in the model. A case study is provided to show the effectiveness of the proposed model. <br /

    TRULLO - local trust bootstrapping for ubiquitous devices

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    Handheld devices have become sufficiently powerful that it is easy to create, disseminate, and access digital content (e.g., photos, videos) using them. The volume of such content is growing rapidly and, from the perspective of each user, selecting relevant content is key. To this end, each user may run a trust model - a software agent that keeps track of who disseminates content that its user finds relevant. This agent does so by assigning an initial trust value to each producer for a specific category (context); then, whenever it receives new content, the agent rates the content and accordingly updates its trust value for the producer in the content category. However, a problem with such an approach is that, as the number of content categories increases, so does the number of trust values to be initially set. This paper focuses on how to effectively set initial trust values. The most sophisticated of the current solutions employ predefined context ontologies, using which initial trust in a given context is set based on that already held in similar contexts. However, universally accepted (and time invariant) ontologies are rarely found in practice. For this reason, we propose a mechanism called TRULLO (TRUst bootstrapping by Latently Lifting cOntext) that assigns initial trust values based only on local information (on the ratings of its user’s past experiences) and that, as such, does not rely on third-party recommendations. We evaluate the effectiveness of TRULLO by simulating its use in an informal antique market setting. We also evaluate the computational cost of a J2ME implementation of TRULLO on a mobile phone

    From Manifesta to Krypta: The Relevance of Categories for Trusting Others

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    In this paper we consider the special abilities needed by agents for assessing trust based on inference and reasoning. We analyze the case in which it is possible to infer trust towards unknown counterparts by reasoning on abstract classes or categories of agents shaped in a concrete application domain. We present a scenario of interacting agents providing a computational model implementing different strategies to assess trust. Assuming a medical domain, categories, including both competencies and dispositions of possible trustees, are exploited to infer trust towards possibly unknown counterparts. The proposed approach for the cognitive assessment of trust relies on agents' abilities to analyze heterogeneous information sources along different dimensions. Trust is inferred based on specific observable properties (Manifesta), namely explicitly readable signals indicating internal features (Krypta) regulating agents' behavior and effectiveness on specific tasks. Simulative experiments evaluate the performance of trusting agents adopting different strategies to delegate tasks to possibly unknown trustees, while experimental results show the relevance of this kind of cognitive ability in the case of open Multi Agent Systems

    The emergence of knowledge exchange: an agent-based model of a software market.

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    We investigate knowledge exchange among commercial organisations, the rationale behind it and its effects on the market. Knowledge exchange is known to be beneficial for industry, but in order to explain it, authors have used high level concepts like network effects, reputation and trust. We attempt to formalise a plausible and elegant explanation of how and why companies adopt information exchange and why it benefits the market as a whole when this happens. This explanation is based on a multi-agent model that simulates a market of software providers. Even though the model does not include any high-level concepts, information exchange naturally emerges during simulations as a successful profitable behaviour. The conclusions reached by this agent-based analysis are twofold: (1) A straightforward set of assumptions is enough to give rise to exchange in a software market. (2) Knowledge exchange is shown to increase the efficiency of the marketAgent-based Computational Economics, adaptive behaviour, knowledge sharing, market efficiency

    A Multi-Dimensional Trust Model for Heterogeneous Contract Observations

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    In this paper we develop a novel probabilistic model of computational trust that allows agents to exchange and combine reputation reports over heterogeneous, correlated multi-dimensional contracts. We consider the specific case of an agent attempting to procure a bundle of services that are subject to correlated quality of service failures (e.g. due to use of shared resources or infrastructure), and where the direct experience of other agents within the system consists of contracts over different combinations of these services. To this end, we present a formalism based on the Kalman filter that represents trust as a vector estimate of the probability that each service will be successfully delivered, and a covariance matrix that describes the uncertainty and correlations between these probabilities. We describe how the agents’ direct experiences of contract outcomes can be represented and combined within this formalism, and we empirically demonstrate that our formalism provides significantly better trustworthiness estimates than the alternative of using separate single-dimensional trust models for each separate service (where information regarding the correlations between each estimate is lost)

    Towards Task Allocation Decision Support by means of Cognitive Modeling of Trust

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    An important issue in research on human-machine cooperation concerns how tasks should be dynamically allocated within a human-machine team in order to improve team performance. The ability to support humans in task allocation decision making requires a thorough understanding of its underlying cognitive processes, and that of relative trust more specifically. This paper presents a computational agent-based model of these cognitive processes and proposes an experiment design that can be used to validate theoretical aspects of this model

    Ethical trust and social moral norms simulation : a bio-inspired agent-based modelling approach

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    The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms

    An interpretable approach for social network formation among heterogeneous agents

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    Understanding the mechanisms of network formation is central in social network analysis. Network formation has been studied in many research fields with their different focuses; for example, network embedding algorithms in machine learning literature consider broad heterogeneity among agents while the social sciences emphasize the interpretability of link formation mechanisms. Here we propose a social network formation model that integrates methods in multiple disciplines and retain both heterogeneity and interpretability. We represent each agent by an “endowment vector” that encapsulates their features and use game-theoretical methods to model the utility of link formation. After applying machine learning methods, we further analyze our model by examining micro- and macro- level properties of social networks as most agent-based models do. Our work contributes to the literature on network formation by combining the methods in game theory, agent-based modeling, machine learning, and computational sociology.King Abdulaziz City of Science and Technology (Saudia Arabia)MIT Trust Data Consortiu

    The Role of Emotion in Elimination of Contribution and Collaboration Dilemma in Citarum River Basin Problem

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    The purposes of the current research are to identify, analyze and simulate the dynamics of interaction and conflicts among agents using drama theory in Citarum river basin problem. To accomplish these purposes, we crate a simulation model that combine drama theory and emotional state model (PAD model). Drama theory was adopted because it able to describe dilemmas and paradox arising from rational goal seeking behavior. It also provides us with rigorous analytical and computational tools for conflict analysis. Our previous model was able to recognize and solve confrontation dilemmas, i.e., persuasion and rejection dilemma among the agents. In this paper, we propose an enhanced simulation model that is able to recognize and solve collaboration dilemmas (trust dilemma) among the agents. In order to obtain some fruitful suggestions for encouraging agent’s collaboration, we product agent-based simulation using SOARS (Spot Oriented Agent Role Simulator).Keywords: Agent based Simulation, Negotiation, Dilemma, Drama Theory, Emotio
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