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Abstract. 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. The art 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 parameters, allowing researchers to perform customizable, easily-repeatable experiments. In the Testbed's artwork appraisal domain, agents, who valuate paintings for clients, may purchase opinions and reputation information from other agents to produce accurate appraisals. The art Testbed features useful data collection tools for storing, downloading, and replaying game data for experimental analysis.

An agent's trust decision strategy consists of the agent's policies for making trust-related decisions, such as who to trust, how trustworthy to be, what reputations to believe, and when to tell truthful reputations. In reputation exchange networks, learning trust decision strategies is complex, compared to non-reputationcommunicating systems. When potential partners may exchange reputation information about an agent, the agent's interactions with one partner are no longer independent from interactions with another; partners may tell each other about their experiences with the agent, influencing future behavior. This research enumerates the types of decisions an agent faces in reputation exchange networks, explains the interdependencies between these decisions, and correlates rewards to each decision. Experimental results using the Agent Reputation and Trust (ART) Testbed demonstrate the success of strategy-learning agents over agents employing naive strategies. The variation in performance of reputation-based learning vs. experience-based learning over different opponents illustrates the need to dynamically determine when to utilize reputations vs. experience in making trust decisions.

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