Generating HomeAssistant Automations Using an LLM-based Chatbot
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913820819062784 |
|---|---|
| author | Giudici, Mathyas Sironi, Alessandro Villa, Ismaele Scherini, Samuele Garzotto, Franca |
| author_facet | Giudici, Mathyas Sironi, Alessandro Villa, Ismaele Scherini, Samuele Garzotto, Franca |
| contents | To combat climate change, individuals are encouraged to adopt sustainable habits, in particular, with their household, optimizing their electrical consumption. Conversational agents, such as Smart Home Assistants, hold promise as effective tools for promoting sustainable practices within households. Our research investigated the application of Large Language Models (LLM) in enhancing smart home automation and promoting sustainable household practices, specifically using the HomeAssistant framework. In particular, it highlights the potential of GPT models in generating accurate automation routines. While the LLMs showed proficiency in understanding complex commands and creating valid JSON outputs, challenges such as syntax errors and message malformations were noted, indicating areas for further improvement. Still, despite minimal quantitative differences between "green" and "no green" prompts, qualitative feedback highlighted a positive shift towards sustainability in the routines generated with environmentally focused prompts. Then, an empirical evaluation (N=56) demonstrated that the system was well-received and found engaging by users compared to its traditional rule-based counterpart. Our findings highlight the role of LLMs in advancing smart home technologies and suggest further research to refine these models for broader, real-world applications to support sustainable living. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02802 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generating HomeAssistant Automations Using an LLM-based Chatbot Giudici, Mathyas Sironi, Alessandro Villa, Ismaele Scherini, Samuele Garzotto, Franca Human-Computer Interaction To combat climate change, individuals are encouraged to adopt sustainable habits, in particular, with their household, optimizing their electrical consumption. Conversational agents, such as Smart Home Assistants, hold promise as effective tools for promoting sustainable practices within households. Our research investigated the application of Large Language Models (LLM) in enhancing smart home automation and promoting sustainable household practices, specifically using the HomeAssistant framework. In particular, it highlights the potential of GPT models in generating accurate automation routines. While the LLMs showed proficiency in understanding complex commands and creating valid JSON outputs, challenges such as syntax errors and message malformations were noted, indicating areas for further improvement. Still, despite minimal quantitative differences between "green" and "no green" prompts, qualitative feedback highlighted a positive shift towards sustainability in the routines generated with environmentally focused prompts. Then, an empirical evaluation (N=56) demonstrated that the system was well-received and found engaging by users compared to its traditional rule-based counterpart. Our findings highlight the role of LLMs in advancing smart home technologies and suggest further research to refine these models for broader, real-world applications to support sustainable living. |
| title | Generating HomeAssistant Automations Using an LLM-based Chatbot |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.02802 |