It is often challenging for a user to articulate their preferences accurately
in multi-objective decision-making problems. Demonstration-based preference
inference (DemoPI) is a promising approach to mitigate this problem.
Understanding the behaviours and values of energy customers is an example of a
scenario where preference inference can be used to gain insights into the
values of energy customers with multiple objectives, e.g. cost and comfort. In
this work, we applied the state-of-art DemoPI method, i.e., the dynamic
weight-based preference inference (DWPI) algorithm in a multi-objective
residential energy consumption setting to infer preferences from energy
consumption demonstrations by simulated users following a rule-based approach.
According to our experimental results, the DWPI model achieves accurate
demonstration-based preference inferring in three scenarios. These advancements
enhance the usability and effectiveness of multi-objective reinforcement
learning (MORL) in energy management, enabling more intuitive and user-friendly
preference specifications, and opening the door for DWPI to be applied in
real-world settings.

通过应用基于演示的偏好推断方法在多目标住宅能源消耗环境中，采用动态权重的推断方法实现了精确的基于演示的偏好推断，提高了能源管理中多目标强化学习 (MORL) 的可用性和效果，为真实世界的应用开辟了新的可能性。

多目标住宅能源管理中从示范推断偏好

Inferring Preferences from Demonstrations in Multi-Objective Residential  Energy Management

During cluster analysis domain experts and visual analysis are frequently
relied on to identify the optimal clustering structure. This process tends to
be adhoc, subjective and difficult to reproduce. This work shows how competency
questions can be used to formalise expert knowledge and application
requirements for context specific evaluation of a clustering application in the
residential energy consumption sector.

本文介绍了如何使用能力问题来规范专家知识和应用需求，以在住宅能源消费领域对聚类应用进行上下文特定评估。