Emergence of norms in interactions with complex rewards
Emergence of norms in interactions with complex rewards
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DOI：
10.1007/s10458-022-09585-3
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发表时间：
2022-10
影响因子：
1.9
通讯作者：
Dhaminda B. Abeywickrama;N. Griffiths;Zhou Xu;Alex Mouzakitis
中科院分区：
文献类型：
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作者：
Dhaminda B. Abeywickrama;N. Griffiths;Zhou Xu;Alex Mouzakitis
自主智能体正变得越来越普遍，并在各种安全关键系统中发挥着越来越大的作用，例如无人驾驶汽车、探索机器人和无人驾驶飞行器。这些代理在高度动态和异构的环境中运行，导致复杂的行为和交互。因此，需要建立模型，并了解更复杂和细致入微的代理人之间的相互作用比以前已经研究。在本文中，我们提出了一种新的基于代理的建模方法来调查规范的出现，在这种相互作用可以进行调查。为此，虽然可能存在一组理想的最佳兼容行为，但也存在具有积极回报且兼容的组合。我们的方法提供了一个步骤，以确定条件下，全球兼容的规范可能会出现在复杂的奖励。我们的模型使用自动驾驶汽车的激励示例进行说明，并且我们提出了自动驾驶车辆在T形交叉口执行左转的场景。
Autonomous agents are becoming increasingly ubiquitous and are playing an increasing role in wide range of safety-critical systems, such as driverless cars, exploration robots and unmanned aerial vehicles. These agents operate in highly dynamic and heterogeneous environments, resulting in complex behaviour and interactions. Therefore, the need arises to model and understand more complex and nuanced agent interactions than have previously been studied. In this paper, we propose a novel agent-based modelling approach to investigating norm emergence, in which such interactions can be investigated. To this end, while there may be an ideal set of optimally compatible actions there are also combinations that have positive rewards and are also compatible. Our approach provides a step towards identifying the conditions under which globally compatible norms are likely to emerge in the context of complex rewards. Our model is illustrated using the motivating example of self-driving cars, and we present the scenario of an autonomous vehicle performing a left-turn at a T-intersection.