In the search and retrieval of multimedia objects, it is impractical to
either manually or automatically extract the contents for indexing since most
of the multimedia contents are not machine extractable, while manual extraction
tends to be highly laborious and time-consuming. However, by systematically
capturing and analyzing the feedback patterns of human users, vital information
concerning the multimedia contents can be harvested for effective indexing and
subsequent search. By learning from the human judgment and mental evaluation of
users, effective search indices can be gradually developed and built up, and
subsequently be exploited to find the most relevant multimedia objects. To
avoid hovering around a local maximum, we apply the epsilon-greedy method to
systematically explore the search space. Through such methodic exploration, we
show that the proposed approach is able to guarantee that the most relevant
objects can always be discovered, even though initially it may have been
overlooked or not regarded as relevant. The search behavior of the present
approach is quantitatively analyzed, and closed-form expressions are obtained
for the performance of two variants of the epsilon-greedy algorithm, namely
EGSE-A and EGSE-B. Simulations and experiments on real data set have been
performed which show good agreement with the theoretical findings. The present
method is able to leverage exploration in an effective way to significantly
raise the performance of multimedia information search, and enables the certain
discovery of relevant objects which may be otherwise undiscoverable.

通过系统捕捉和分析人类用户的反馈模式，可以对多媒体内容进行有效索引，并使用 epsilon-greedy 方法避免本地最大值，并提高多媒体信息检索性能。

使用 Epsilon-Greedy 算法的多媒体信息搜索性能有效性研究

Performance Effectiveness of Multimedia Information Search Using the  Epsilon-Greedy Algorithm

We study adversarial attacks that manipulate the reward signals to control
the actions chosen by a stochastic multi-armed bandit algorithm. We propose the
first attack against two popular bandit algorithms: $\epsilon$-greedy and UCB,
\emph{without} knowledge of the mean rewards. The attacker is able to spend
only logarithmic effort, multiplied by a problem-specific parameter that
becomes smaller as the bandit problem gets easier to attack. The result means
the attacker can easily hijack the behavior of the bandit algorithm to promote
or obstruct certain actions, say, a particular medical treatment. As bandits
are seeing increasingly wide use in practice, our study exposes a significant
security threat.

该论文研究了对多臂赌博算法进行的对抗攻击，以操纵奖励信号以控制算法选择的行动，并提出了针对常见的两种多臂赌博算法 epsilon-greedy 和 UCB 的攻击方案。这种攻击是在不知道平均奖励的情况下进行的，并且攻击者所需的努力是对问题特定参数取对数，这个参数随着赌博问题变得越来越容易攻击而变小。结果表明，攻击者可以轻松地劫持多臂赌博算法的行为，以推广或阻止某些行动。由于多臂赌博算法在实践中的使用越来越广泛，因此我们的研究揭示了一个重大的安全威胁。