Loading Now

Summary of Hkd-sho: a Hybrid Smart Home System Based on Knowledge-based and Data-driven Services, by Mingming Qiu et al.


HKD-SHO: A hybrid smart home system based on knowledge-based and data-driven services

by Mingming Qiu, Elie Najm, Rémi Sharrock, Bruno Traverson

First submitted to arxiv on: 15 Feb 2024

Categories

  • Main: Artificial Intelligence (cs.AI)
  • Secondary: Machine Learning (cs.LG)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
A novel hybrid system called HKD-SHO (Hybrid Knowledge-based and Data-driven services based Smart HOme system) integrates knowledge-based and machine learning-based data-driven approaches to create dynamic smart home services. This system addresses limitations in existing methods, including the need for manual input and the lack of transparency in service recommendations. By combining the explicability of knowledge-based services with the dynamism of data-driven services, HKD-SHO can effectively provide inhabitants with tailored suggestions for adjusting actuators to achieve target values of monitored states.
Low GrooveSquid.com (original content) Low Difficulty Summary
Smart homes are becoming a reality by setting up various services. To make these services better, researchers have tried different approaches. Some rely on people’s knowledge and input, while others use machine learning and data analysis. However, these methods have their own limitations. For example, the first approach requires people to know how to adjust things to achieve certain results, which can be complicated. The second approach is like a black box that doesn’t explain why it recommends certain actions. To solve these problems, scientists are proposing a new way of combining both approaches. This system, called HKD-SHO, offers the best of both worlds by being easy to understand and making smart suggestions.

Keywords

* Artificial intelligence  * Machine learning