Summary of Inferring Preferences From Demonstrations in Multi-objective Residential Energy Management, by Junlin Lu et al.
Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
by Junlin Lu, Patrick Mannion, Karl Mason
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a promising approach called DemoPI, which aims to mitigate the challenge of accurately articulating preferences in multi-objective decision-making problems. The authors apply the state-of-the-art DemoPI method, 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. The results show that the DWPI model achieves accurate demonstration-based preference inference in three scenarios, enhancing the usability and effectiveness of MORL in energy management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand what people want when it comes to energy usage. Imagine you’re trying to decide how to use your electricity, balancing cost and comfort. The authors came up with a way to figure out what people prefer by looking at how they behave. They tested this method in a fake energy consumption setting and showed that it works well. This is important because it can help make energy management more user-friendly. |
Keywords
» Artificial intelligence » Inference
