A Survey of Multi-Agent Trust Management Systems
TL;DR: Existing trust models from a game theoretic perspective are analyzed to highlight the special implications of including human beings in an MAS, and a possible research agenda to advance the state of the art in this field is proposed.
Abstract: In open and dynamic multiagent systems (MASs), agents often need to rely on resources or services provided by other agents to accomplish their goals. During this process, agents are exposed to the risk of being exploited by others. These risks, if not mitigated, can cause serious breakdowns in the operation of MASs and threaten their long-term wellbeing. To protect agents from the uncertainty in the behavior of their interaction partners, the age-old mechanism of trust between human beings is re-contexted into MASs. The basic idea is to let agents self-police the MAS by rating each other on the basis of their observed behavior and basing future interaction decisions on such information. Over the past decade, a large number of trust management models were proposed. However, there is a lack of research effort in several key areas, which are critical to the success of trust management in MASs where human beings and agents coexist. The purpose of this paper is to give an overview of existing research in trust management in MASs. We analyze existing trust models from a game theoretic perspective to highlight the special implications of including human beings in an MAS, and propose a possible research agenda to advance the state of the art in this field.
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TL;DR: The architecture and features of fog computing are reviewed and critical roles of fog nodes are studied, including real-time services, transient storage, data dissemination and decentralized computation, which are expected to draw more attention and efforts into this new architecture.
Abstract: Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.
499 citations
Cites background from "A Survey of Multi-Agent Trust Manag..."
...A large number of trust management mechanisms [115], [116], [119], [120] have been proposed to analyze trust relationship under two trust models [115]: evidence-based trust model and monitoring-based trust model....
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...In evidence-based trust model, any witness that proves trust relationship among users is exploited to build the trustworthiness, such as public key, address, identity, or any evidence that an user can generate for itself or other users [116]....
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TL;DR: The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature, and to discuss of open problems which may form the basis for fruitful future research.
389 citations
Cites background from "A Survey of Multi-Agent Trust Manag..."
...This question has been addressed extensively by researchers in behavioural game theory and experimental psychology (Camerer et al., 2015; Goodie et al., 2012; Wright and Leyton-Brown, 2010; Yoshida et al., 2008; Camerer et al., 2004; Hedden and Zhang, 2002)....
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TL;DR: This survey provides a comprehensive discussion of all aspects of MAS, starting from definitions, features, applications, challenges, and communications to evaluation, and a classification on MAS applications and challenges is provided.
Abstract: Multi-agent systems (MASs) have received tremendous attention from scholars in different disciplines, including computer science and civil engineering, as a means to solve complex problems by subdividing them into smaller tasks. The individual tasks are allocated to autonomous entities, known as agents. Each agent decides on a proper action to solve the task using multiple inputs, e.g., history of actions, interactions with its neighboring agents, and its goal. The MAS has found multiple applications, including modeling complex systems, smart grids, and computer networks. Despite their wide applicability, there are still a number of challenges faced by MAS, including coordination between agents, security, and task allocation. This survey provides a comprehensive discussion of all aspects of MAS, starting from definitions, features, applications, challenges, and communications to evaluation. A classification on MAS applications and challenges is provided along with references for further studies. We expect this paper to serve as an insightful and comprehensive resource on the MAS for researchers and practitioners in the area.
290 citations
Additional excerpts
...Distributed trust is widely used inMAS to enhance security whereby agents build trust in each other by observing and verifying their actions [150], [151]....
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TL;DR: Clinicians as the primary users of AI systems in health care are focused on and factors shaping trust between clinicians and AI are presented, highlighting critical challenges related to trust that should be considered during the development of any AI system for clinical use.
Abstract: Artificial intelligence (AI) can transform health care practices with its increasing ability to translate the uncertainty and complexity in data into actionable-though imperfect-clinical decisions or suggestions In the evolving relationship between humans and AI, trust is the one mechanism that shapes clinicians' use and adoption of AI Trust is a psychological mechanism to deal with the uncertainty between what is known and unknown Several research studies have highlighted the need for improving AI-based systems and enhancing their capabilities to help clinicians However, assessing the magnitude and impact of human trust on AI technology demands substantial attention Will a clinician trust an AI-based system? What are the factors that influence human trust in AI? Can trust in AI be optimized to improve decision-making processes? In this paper, we focus on clinicians as the primary users of AI systems in health care and present factors shaping trust between clinicians and AI We highlight critical challenges related to trust that should be considered during the development of any AI system for clinical use
202 citations
Cites background from "A Survey of Multi-Agent Trust Manag..."
...This attitude may be intrinsically formed based on the user’s own experience with the system of interest or may stem from an extrinsic source such as the reputation of the system in the user’s social circle [14]....
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TL;DR: This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning, focusing primarily on literature from recent years that combinesDeep reinforcement learning methods with a multi- agent scenario.
Abstract: The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity. This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning. We focus primarily on literature from recent years that combines deep reinforcement learning methods with a multi-agent scenario. To survey the works that constitute the contemporary landscape, the main contents are divided into three parts. First, we analyze the structure of training schemes that are applied to train multiple agents. Second, we consider the emergent patterns of agent behavior in cooperative, competitive and mixed scenarios. Third, we systematically enumerate challenges that exclusively arise in the multi-agent domain and review methods that are leveraged to cope with these challenges. To conclude this survey, we discuss advances, identify trends, and outline possible directions for future work in this research area.
180 citations
References
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Book•
01 Jan 1988TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
37,989 citations
"A Survey of Multi-Agent Trust Manag..." refers methods in this paper
...In [26], an approach based on theQ-learning technique [79] is proposed to select an appropriate γ value from a predetermined static set, 0, of values....
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4,805 citations
"A Survey of Multi-Agent Trust Manag..." refers methods in this paper
...The proposed model—TAUCA—combines a variant of the cumulative sum (CUSUM) approach [83] that identifies the point in time when possible changes in witness behavior patterns occur with correlation analysis to filter out suspicious testimonies....
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01 Mar 2007
TL;DR: Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision as mentioned in this paper, where the basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score.
Abstract: Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems.
3,493 citations
TL;DR: In this paper, the authors define and discuss several notions of potential functions for games in strategic form, and characterize games that have a potential function, and present a variety of applications.
2,367 citations
Dissertation•
01 Jan 1994
TL;DR: The thesis presents a testbed populated by simple trusting agents which substantiates the utility of the formalism and provides a step in the direction of a proper understanding and definition of human trust.
Abstract: Trust is a judgement of unquestionable utility — as humans we use it every day of our lives. However, trust has suffered from an imperfect understanding, a plethora of definitions, and informal use in the literature and in everyday life. It is common to say “I trust you,” but what does that mean? This thesis provides a clarification of trust. We present a formalism for trust which provides us with a tool for precise discussion. The formalism is implementable: it can be embedded in an artificial agent, enabling the agent to make trust-based decisions. Its applicability in the domain of Distributed Artificial Intelligence (DAI) is raised. The thesis presents a testbed populated by simple trusting agents which substantiates the utility of the formalism. The formalism provides a step in the direction of a proper understanding and definition of human trust. A contribution of the thesis is its detailed exploration of the possibilities of future work in the area.
1,660 citations
"A Survey of Multi-Agent Trust Manag..." refers background in this paper
...Trust was first introduced as a measurable property of an entity in computer science in [65]....
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