Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part II
Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part II
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人工智能。
DOI：
10.1007/978-3-031-50485-3_25
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Shafipour E
中科院分区：
文献类型：
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作者：
Shafipour E
在本文中，我们开发了一种新的方法来帮助电动汽车（EV）的驾驶员在长途旅行中计划充电站。这里的一个关键挑战是引发驾驶员高度异质性的偏好。在这里，我们开发了一个智能的个人代理，通过多种交互来学习偏好。为了最大限度地减少驾驶员的认知负担，我们提出了一种新的技术，它适用于一个小规模的离散选择实验与司机互动。具体地，代理基于驾驶员关于驾驶员偏好的最新信念向驾驶员提供具有充电站的可能组合的若干路线。然后，通过后续的迭代，个人代理学习并改进其关于驾驶员偏好的信念。它建议更好的路线更接近司机的喜好。我们评估了我们的新算法与电动汽车司机的真实的偏好数据，表明我们的方法快速收敛到最佳路线后，只有少量的查询。
In this paper, we develop a novel approach to help drivers of electric vehicles (EVs) plan charging stops on long journeys. A key challenge here is eliciting the highly heterogeneous preferences of drivers. Here we develop an intelligent personal agent that learns preferences through multiple interactions. To minimise the cognitive burden on the driver, we propose a novel technique which applies a small-scale discrete choice experiment to interact with the driver. Specifically, the agent provides drivers with several routes with possible combinations of charging stops based on their latest beliefs about the driver’s preferences. Then, through subsequent iterations, the personal agent learns and refines its beliefs about the driver’s preferences. It suggests better routes closer to the driver’s preferences. We evaluated our novel algorithm with real preference data from EV drivers, showing that our approach converges quickly to the optimal routes after only a small number of queries.