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首页> 外文会议>First International Conference on Artificial intelligence, modelling amp; simulation >Applying Multi-armed Bandit Strategies to Change of Collaboration Patterns at Runtime
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Applying Multi-armed Bandit Strategies to Change of Collaboration Patterns at Runtime

机译：在运行时将多臂强盗策略应用于协作模式的更改

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摘要

Autonomic systems have some interesting properties that enable them to self-manage at runtime. Traditionally, they are Self-configuration, Self-healing, Self-optimization, and Self-protection. Another interesting property is Self-expression, i.e. The capability of reconfiguring the collaboration pattern of a system at runtime and in an autonomous way. This re-factoring of behaviors, roles and interactions takes place whenever the environment in which the set of components is inserted experiences some changes, so that in order to adapt to the ever variable external conditions, the system is still able to maximize its utilities. In the past we have motivated the need for such a pattern change, and have proposed some approaches to enact it in situations in which we can associate to each external condition the fittest coordination pattern. In this paper we propose a strategy to decide the change and to choose the next collaboration pattern in situations in which the system components do not have sufficient knowledge and cognition to reason about the varying external conditions. Our approach is based on the algorithms proposed to solve the multi-armed bandit problem, in which a player must choose which slot machine to pull, given a number of them, in order to maximize the reward.
机译：自主系统具有一些有趣的属性，使它们可以在运行时进行自我管理。传统上，它们是自我配置，自我修复，自我优化和自我保护。另一个有趣的属性是自我表达，即在运行时以自主方式重新配置系统协作模式的能力。每当插入组件集的环境发生某些变化时，都会进行行为，角色和交互的重构，因此，为了适应不断变化的外部条件，系统仍然能够最大限度地发挥其效用。过去，我们已经激发了对这种模式更改的需求，并提出了一些方法来在可以将每种外部条件与最适度的协调模式相关联的情况下实施它。在本文中，我们提出了一种策略来决定更改，并在系统组件没有足够的知识和认知来推断外部条件变化的情况下选择下一个协作模式。我们的方法基于为解决多臂匪徒问题而提出的算法，在这种算法中，玩家必须选择给定数量的老虎机才能拉动老虎机，以最大化奖励。

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