Notions of reputation in multi-agents systems: a review
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
An evidential model of distributed reputation management
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
On Multiagent Q-Learning in a Semi-Competitive Domain
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Best-Response Multiagent Learning in Non-Stationary Environments
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Challenges for trust, fraud and deception research in multi-agent systems
AAMAS'02 Proceedings of the 2002 international conference on Trust, reputation, and security: theories and practice
Dynamically learning sources of trust information: experience vs. reputation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Agent trust evaluation and team formation in heterogeneous organizations
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Learning task-specific trust decisions
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Beyond Accuracy. Reputation for Partner Selection with Lies and Retaliation
Multi-Agent-Based Simulation VIII
Learning to trust in the competence and commitment of agents
Autonomous Agents and Multi-Agent Systems
Action-Based Environment Modeling for Maintaining Trust
Trust in Agent Societies
Art Competition: Agent Designs to Handle Negotiation Challenges
Trust in Agent Societies
Evidence-based trust: A mathematical model geared for multiagent systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Towards a framework for trusting the automated learning of social ontologies
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Cooperation through reciprocity in multiagent systems: an evolutionary analysis
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Evolving Cooperation through Reciprocity Using a Centrality-Based Reputation System
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
The agent reputation and trust (ART) testbed
iTrust'06 Proceedings of the 4th international conference on Trust Management
Data in social network analysis
ICCMSN'08 Proceedings of the First international conference on Computer-Mediated Social Networking
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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-reputation-communicating 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.