In systems with multiple potentially deceptive agents, any single agent may have to assess the trustworthiness of other agents in order to decide with which agents to interact. In this context, indirect trust refers to trust established through third-party advice. Since the advisers themselves may be deceptive or unreliable, agents need a mechanism to assess and properly incorporate advice. We evaluate existing state-of-the-art methods for computing indirect trust in numerous simulations, demonstrating that the best ones tend to be of prohibitively large complexity. We propose a new and easy to implement method for computing indirect trust, based on a simple prediction with expert advice strategy as is often used in online learning. This method either competes with or outperforms all tested systems in the vast majority of the settings we simulated, while scaling substantially better. Our results demonstrate that existing systems for computing indirect trust are overly complex; the problem can be solved much more efficiently than the literature suggests.

In multi-agent systems, agents often have to rely on interactions with other agents in order to accomplish a given task. They hence need to assess the trustworthiness of other agents, which is particularly difficult if the latter change their behavior dynamically. The two common techniques to solve this problem are Hidden Markov Models (HMMs) and standard Beta Reputation Systems (BRS) equipped with a simple decay mechanism to discount older interactions. We propose instead to use Page-Hinkley statistics in BRS to detect and dismiss an agent whose behavior worsens. Our experimental study demonstrates that our method outperforms HMMs and, in the vast majority of tested settings, either outperforms or is on par with other typically used BRS-type methods.

To assess the trustworthiness of an agent in a multi-agent system, one often combines two types of trust information: direct trust information derived from one's own interactions with that agent, and indirect trust information based on advice from other agents. This paper provides the first systematic study on when it is beneficial to combine these two types of trust as opposed to relying on only one of them. Our large-scale experimental study shows that strong methods for computing indirect trust make direct trust redundant in a surprisingly wide variety of scenarios. Further, a new method for the combination of the two trust types is proposed that, in the remaining scenarios, outperforms the ones known from the literature.

AdaBoost is a highly popular ensemble classification method for which many variants have been published. This paper proposes a generic refinement of all of these AdaBoost variants. Instead of assigning weights based on the total error of the base classifiers (as in AdaBoost), our method uses class-specific error rates. On instance x it assigns a higher weight to a classifier predicting label y on x, if that classifier is less likely to make a mistake when it predicts class y. Like AdaBoost, our method is guaranteed to boost weak learners into strong learners. An empirical study on AdaBoost and one of its multi-class versions, SAMME, demonstrates the superiority of our method on datasets with more than 1,000 instances as well as on datasets with more than three classes.

No abstract