Simulated trust : towards robust social learning

Social learning is a potentially powerful learning mechanism to use in artificial multi-agent systems. However, findings about how animals use social learning show that it is also possibly detrimental. By using social learning agents act based on second-hand information that might not be trustworthy. This can lead to the spread of maladaptive behavior throughout populations. Animals employ a number of strategies to selectively use social learning only when appropriate. This suggests that artificial agents could learn more successfully if they are able to strike the appropriate balance between social and individual learning. In this paper, we propose a simple mechanism that regulates the extent to which agents rely on social learning. Our agents can vary the amount of trust they have in others. The trust is not determined by the performance of others but depends exclusively on the agents’ own rating of the demonstrations. The effectiveness of this mechanism is examined through a series of simulations. We first show that there are various circumstances under which the performance of multi-agents systems is indeed seriously hampered when agents rely on indiscriminate social learning. We then investigate how agents that incorporate the proposed trust mechanism fare under the same circumstances. Our simulations indicate that the mechanism is quite effective in regulating the extent to which agents rely on social learning. It causes considerable improvements in the learning rate, and can, under some circumstances, even improve the eventual performance of the agents. Finally, some possible extensions of the proposed mechanism are being discussed.