Trust and Reputation in Multi-Agent Systems.
PhD thesis, Concordia University.
Multi-Agent systems (MAS) are artificial societies populated with
distributed autonomous agents that are intelligent and rational.
These self-independent agents are capable of independent decision
making towards their predefined goals. These goals might be common
between agents or unique for an agent. Agents may cooperate with
one another to facilitate their progresses. One of the fundamental
challenges in such settings is that agents do not have a full
knowledge over the environment and regarding their decision making
processes, they might need to request other agents for a piece of
information or service. The crucial issues are then how to rely on
the information provided by other agents, how to consider the
collected data, and how to select appropriate agents to ask for
the required information. There are some proposals addressing how
an agent can rely on other agents and how an agent can compute the
overall opinion about a particular agent. In this context, the
trust value reflects the extent to which agents can rely on other
agents and the reputation value represents public opinion about a
particular agent. Existing approaches for reliable information
propagation fail to capture the dynamic relationships between
agents and their influence on further decision making process.
Therefore, these models fail to adapt agents to frequent
environment changes. In general, a well-founded trust and
reputation system that prevents malicious acts that are emerged by
selfish agents is required for multi-agent systems. We propose a
trust mechanism that measures and analyzes the reliability of
agents cooperating with one another. This mechanism concentrates
on the key attributes of the related agents and their
relationships. We also measure and analyze the public reputation
of agents in large-scale environments utilizing a sound reputation
mechanism. In this mechanism, we aim at maintaining a public
reputation assessment in which the public actions of agents are
accurately under analysis. On top of the theoretical analysis, we
experimentally validate our trust and reputation approaches
through different simulations. Our preliminary results show that
our approach outperforms current frameworks in providing accurate
credibility measurements and maintaining accurate trust and
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