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Mohammad Hossein Nikravan

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3 papers

1 author row

AAAI Conference 2021 Conference Paper

Precision-based Boosting

Mohammad Hossein Nikravan
Marjan Movahedan
Sandra Zilles

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 classspecific 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 Ada- Boost, 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.

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IJCAI Conference 2020 Conference Paper

Combining Direct Trust and Indirect Trust in Multi-Agent Systems

Elham Parhizkar
Mohammad Hossein Nikravan
Robert C. Holte
Sandra Zilles

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.

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IJCAI Conference 2019 Conference Paper

Indirect Trust is Simple to Establish

Elham Parhizkar
Mohammad Hossein Nikravan
Sandra Zilles

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.

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