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A diverse collection of trust-modeling algorithms for multi-agent systems has been developed in recent years, resulting in significant breadth-wise growth without unified direction or benchmarks. Based on enthusiastic response from the agent trust community, the Agent Reputation and Trust (ART) Testbed initiative has been launched, charged with the task of(More)
Trust is essential when an agent must rely on others to provide resources for accomplishing its goals. When deciding whether to trust, an agent may rely on, among other types of trust information, its past experience with the trustee or on reputations provided by third-party agents. However, each type of trust information has strengths and weaknesses: trust(More)
The Agent Reputation and Trust (ART) Testbed initiative has been launched with the goal of establishing a testbed for agent reputation-and trust-related technologies. This testbed serves in two roles: (1) as a competition forum in which researchers can compare their technologies against objective metrics, and (2) as a suite of tools with flexible(More)
The Agent Reputation and Trust (ART) Testbed [1] provides functionality for researchers of trust and reputation in multi-agent systems. As a versatile, universal experimentation site, the ART Testbed scopes relevant trust research problems and unites researchers toward solutions via unified experimentation methods. Through objective, well-defined metrics,(More)
Accounting for social, cultural, and political factors must form the basis for understanding decision-making, actions, and reactions of individuals, thus driving their behaviors and intentions. Clearly, the individual is not wholly defined by just personal social, cultural, and political beliefs but also functions within a group of individuals. Within these(More)