Learning Task-Specific Trust Decisions (Short Paper)

We study the problem of agents locating other agents that are both capable and willing to help complete assigned tasks. An agent incurs a fixed cost for each help request it sends out. To minimize this cost, the performance metric used in our work, an agent should learn based on past interactions to identify agents likely to help on a given task. We compare three trust mechanisms: success-based, learning-based, and random. We also consider different agent social attitudes: selfish, reciprocative, and helpful. We evaluate the performance of these social attitudes with both homogeneous and mixed societies. Our results show that learning-based trust decisions consistently performed better than other schemes. We also observed that the success rate is significantly better for reciprocative agents over selfish agents.