Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reputation in Artificial Societies: Social Beliefs for Social Order
Reputation in Artificial Societies: Social Beliefs for Social Order
Building trust in online auction markets through an economic incentive mechanism
Decision Support Systems
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Honesty and trust revisited: the advantages of being neutral about other's cognitive models
Autonomous Agents and Multi-Agent Systems
Strategies for exploiting trust models in competitive multi-agent systems
MATES'09 Proceedings of the 7th German conference on Multiagent system technologies
| Hi-index | 0.00 |
In this paper we propose the design of an agent for the ART Testbed, a tool created with the goal of objectively evaluate different trust strategies. The agent design includes a trust model and a strategy for decision making. The trust model is based on the three components of trust considered in ART, namely direct, indirect (reputation) and self trust (certainty). It also incorporates a variable time window size based on the available information that allows the agent to easily adapt to possible changes in the environment. The decision-making strategy uses the information provided by the trust model to take the best decisions to achieve the most benefits for the agent. This decision making tackles the exploration versus exploitation problem since the agent has to decide when to interact with the known agents and when to look for new ones. The agent, called Uno2008, competed in and won the Third International ART Testbed Competition held at AAMAS in March 2008.