Automatic generation of explanations in autonomous systems: enhancing human interaction in smart home environments
[EN] In smart environments, autonomous systems often adapt their behavior to the context, and although such adaptations are generally beneficial, they may cause users to struggle to understand or trust them. To address this, we propose an explanation generation system that produces natural language...
| Authors: | , , , , |
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| Format: | article |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Universitat Politècnica de València (UPV) |
| Repository: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Language: | English |
| OAI Identifier: | oai:riunet.upv.es:10251/230138 |
| Online Access: | https://riunet.upv.es/handle/10251/230138 |
| Access Level: | Open access |
| Keyword: | Automatic explanations,Autonomous systems User-centered explanations Smart home Home automation Interactive systems Human-centric design Large Language Model Human-machine interaction Context-aware explanations |
| Summary: | [EN] In smart environments, autonomous systems often adapt their behavior to the context, and although such adaptations are generally beneficial, they may cause users to struggle to understand or trust them. To address this, we propose an explanation generation system that produces natural language descriptions (explanations) to clarify the adaptive behavior of smart home systems in runtime. These explanations are customized based on user characteristics and the contextual information derived from the user interactions with the system. Our approach leverages a prompt-based strategy using a fine-tuned large language model, guided by a modular template that integrates key data such as the type of explanation to be generated, user profile, runtime system information, interaction history, and the specific nature of the system adaptation. As a preliminary step, we also present a conceptual model that characterize explanations in the domain of autonomous systems by defining their core concepts. Finally, we evaluate the user experience of the generated explanations through an experiment involving 118 participants. Results show that generated explanations are perceived positive and with high level of acceptance. |
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