Browsing by Author "Marsh, Stephen"

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    Architectural components of information sharing societies
    Carter, Jonathan; Ghorbani, Ali, A.; Marsh, Stephen
    Two similar multiagent systems have been designed to address the issue of information sharing within a multiagent system. This paper examines architectural components that have been added to our information sharing societies, ACORN and MP3. Through this exploration, we conclude that these components and their underlying concepts can be added to other information retrieval societies. ACORN consists of a set of information sharing locations referred to as Cafes. Cafes are defined as meeting locations for like-minded agents. Like-minded agents are defined as agents that share a common set of interests. As an example, a cafe may contain agents that are interested in information relating to cars. A dynamic cafe clustering method is developed. The performance evaluation of the proposed structure for the cafe is presented. The concept of a fat/thin agent architecture is introduced. This agent architecture allows for minimizing network traffic as agents traverse the network in search of or distribution of knowledge. The directory server component is presented along with its relation to the fat/thin agent architecture. Lastly, anonymity service provider which allows anonymity for users is introduced. The MP3 society exists with the sole purpose of finding MP3's throughout a given network. Through this society, the core design issues of agent verification, and agent validation are addressed and solutions are presented through respective interface components. Key words: Agent, fat/thin agents, multiagent systems, clustering, MP3, information sharing
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    The ACORN Multi-Agent System
    (2001) Marsh, Stephen; Ghorbani, Ali; Bhavsar, Virendra, C.
    ACORN (Agent-based Community Oriented Routing Network) is a distributed multi-agent architecture for the search, distribution and management of information across networks. ACORN utilises the concept of 'information as agent' together with an application of Stanley Milgram's Small World Problem (the idea of Six Degrees of Separation) in order to route individual items of information around a network of people and agents. The ACORN ideal is to achieve a state where a web of users is created such that information distribution, queries and search, and browsing behaviour is encapsulated in a single adaptive architecture which learns community behaviour and knowledge in order to route agents to relevant destinations (users). This paper describes the ACORN architecture and its implementation. We introduce a novel idea of agent meeting places, or Cafes, to carry out community-based information sharing among mobile agents in ACORN. ACORN is compared with similar work, and evaluations of ACORN for information sharing among mobile agents are described. Applications of ACORN include Business to Business and Business to Consumer based e-Commerce solutions, virtual community creation and support systems, peer reviewing systems, and personalized directed information handling. Keywords: Multi-Agent information architectures, autonomous agents, mobile agents, keyphrase matching, multi-agent architecture, community based information handling, e-Commerce.
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    zTrust: adaptive decentralized trust model for QoS selection in electronic marketplaces
    (University of New Brunswick, 2013) Noorian, Zeinab; Fleming, Michael; Marsh, Stephen
    In the absence of legal enforcement procedures for the participants of an open e-marketplace, trust and reputation systems are central to resisting threats from malicious agents. Such systems also provide mechanisms for identifying participants who disseminate unfair ratings. This thesis research is in the area of modeling trust in multi-agent systems where self-interested agents intelligently interact to maximize their benefits. The thesis presents an adaptive intelligent trust model, called zTrust, with multidisciplinary approaches well-suited for cooperative and competitive electronic commerce in such a way that individuals could make optimal decisions in selecting business transaction partners. zTrust has two essential elements: 1) a two-layered filtering algorithm, the Prob-Cog model, with an adaptive threshold evaluation procedure, which combines cognitive and probabilistic views of trust to classify participants through modelling their subjectivity, behavioural characteristics and environmental conditions in order to detect unfair advisers, and 2) a two-layered Trust-Oriented Service Selection (TOSS) framework to assist consumers in discovering providers who maximize their utility by achieving sufficient trust and fulfilling consumers' preferences on product quality. We also propose a trust-oriented mechanism built on a game-theoretic basis well-suited for competitive e-marketplaces where providers might have limited inventory. The characteristics of such an environment make consumers concerned with the possibility of losing the opportunity to do business with good providers whilst providing truthful reputation information about providers. The proposed mechanism provides consumers with a means to strategically determine their reporting behaviour by establishing a balance between the possibility of losing business opportunities because of truthful reporting and the possibility of not receiving truthful provider information from advisers if the consumers report untruthfully. We provide a series of experimental results in a simulated dynamic environment where agents may be arriving and departing. We illustrate the efficacy and the robustness of our approaches in important conditions of the cooperative and competitive marketplaces, as well as in comparison with other approaches. We observe that zTrust enables consumers to access more accurate information, even in a hostile environment, and enjoy high profits as a result of interacting with high-quality providers. Furthermore, our mechanism promotes honesty amongst providers as dishonest providers would be isolated and would lose the opportunity to be selected as transaction partners.