S5-HES Agent: Society 5.0-driven Agentic Framework to Democratize Smart Home Environment Simulation
- URL: http://arxiv.org/abs/2603.01554v1
- Date: Mon, 02 Mar 2026 07:30:09 GMT
- Title: S5-HES Agent: Society 5.0-driven Agentic Framework to Democratize Smart Home Environment Simulation
- Abstract summary: This paper presents the Society 5.0-driven Smart Home Environment Simulator Agent (S5-HES Agent)<n>The framework coordinates specialized agents through interchangeable large language models (LLMs)<n>A retrieval-augmented generation (RAG) pipeline with semantic, keyword, and hybrid search retrieves smart home knowledge.
- Score: 1.4680035572775534
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The smart home is a key domain within the Society 5.0 vision for a human-centered society. Smart home technologies rapidly evolve, and research should diversify while remaining aligned with Society 5.0 objectives. Democratizing smart home research would engage a broader community of innovators beyond traditional limited experts. This shift necessitates inclusive simulation frameworks that support research across diverse fields in industry and academia. However, existing smart home simulators require significant technical expertise, offer limited adaptability, and lack automated evolution, thereby failing to meet the holistic needs of Society 5.0. These constraints impede researchers from efficiently conducting simulations and experiments for security, energy, health, climate, and socio-economic research. To address these challenges, this paper presents the Society 5.0-driven Smart Home Environment Simulator Agent (S5-HES Agent), an agentic simulation framework that transforms traditional smart home simulation through autonomous AI orchestration. The framework coordinates specialized agents through interchangeable large language models (LLMs), enabling natural-language-driven end-to-end smart home simulation configuration without programming expertise. A retrieval-augmented generation (RAG) pipeline with semantic, keyword, and hybrid search retrieves smart home knowledge. Comprehensive evaluation on S5-HES Agent demonstrates that the RAG pipeline achieves near-optimal retrieval fidelity, simulated device behaviour and threat scenarios align with real-world IoT datasets, and simulation engine scales predictably across home configurations, establishing a stable foundation for Society 5.0 smart home research. Source code is available under the MIT License at https://github.com/AsiriweLab/S5-HES-Agent.
Related papers
- EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery [51.466674519159824]
We present EurekAgent, an environment-engineered agent system for metric-driven autonomous scientific discovery.<n>We frame this as environment engineering: building environments that amplify productive behaviors, while suppressing harmful behaviors, such as reward hacking and high-friction human oversight.<n>We open-source our code and results, and call for environment engineering as a core research direction for developing reliable autonomous research agents.
arXiv Detail & Related papers (2026-06-11T17:56:35Z) - HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation [22.87553318476621]
We propose HomeFlow to generate high-quality training data for smart home agents.<n>HomeFlow uses HomeEnv as a unified simulation environment and HomeMaker to procedurally generate diverse home settings.<n>HomeFlow-RL-4B and HomeFlow-RL-8B achieve task success rates of 84.60% and 87.03%, respectively.
arXiv Detail & Related papers (2026-05-31T13:26:46Z) - The Agent Use of Agent Beings: Agent Cybernetics Is the Missing Science of Foundation Agents [26.511094818984763]
Cybernetics is the science of control and communication in complex systems.<n>We argue that cybernetics provides the missing theoretical scaffold for foundation agents.
arXiv Detail & Related papers (2026-05-11T15:53:54Z) - TongSIM: A General Platform for Simulating Intelligent Machines [59.27575233453533]
Embodied intelligence focuses on training agents within realistic simulated environments.<n>TongSIM is a high-fidelity, general-purpose platform for training and evaluating embodied agents.
arXiv Detail & Related papers (2025-12-23T10:00:43Z) - Dyna-Mind: Learning to Simulate from Experience for Better AI Agents [62.21219817256246]
We argue that current AI agents need ''vicarious trial and error'' - the capacity to mentally simulate alternative futures before acting.<n>We introduce Dyna-Mind, a two-stage training framework that explicitly teaches (V)LM agents to integrate such simulation into their reasoning.
arXiv Detail & Related papers (2025-10-10T17:30:18Z) - SimuHome: A Temporal- and Environment-Aware Benchmark for Smart Home LLM Agents [10.925787133867951]
SimuHome is a time-accelerated home environment that simulates smart devices, supports API calls, and reflects changes in environmental variables.<n>SimuHome provides a high-fidelity environment, and agents validated in SimuHome can be deployed on real Matter-compliant devices with minimal adaptation.<n>Our evaluation of 11 agents under a unified ReAct framework reveals that while models perform well on simple tasks, they struggle with latent intent inference, state verification, and especially temporal scheduling.
arXiv Detail & Related papers (2025-09-29T04:54:20Z) - SOCIA: Joint Structure-Parameter Co-Optimization for Automated Simulator Construction [15.391456718509282]
Simulation Orchestration for Computational Intelligence with Agents is a framework that treats simulator construction as joint structure- parameter co-optimization.<n>It elicits mechanism-rich blueprints, exposes explicit tunable parameters, and instantiates a calibration schema.<n>We will release the code soon.
arXiv Detail & Related papers (2025-05-17T13:47:31Z) - YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models [50.35333054932747]
We introduce a novel social simulator called YuLan-OneSim.<n>Users can simply describe and refine their simulation scenarios through natural language interactions with our simulator.<n>We implement 50 default simulation scenarios spanning 8 domains, including economics, sociology, politics, psychology, organization, demographics, law, and communication.
arXiv Detail & Related papers (2025-05-12T14:05:17Z) - AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society [32.849311155921264]
We propose AgentSociety, a large-scale social simulator that integrates a realistic societal environment.<n>Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions.<n>We focus on four key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, and the impact of external shocks such as hurricanes.
arXiv Detail & Related papers (2025-02-12T15:27:07Z) - Toward a Cohesive AI and Simulation Software Ecosystem for Scientific Innovation [2.0580344655030554]
We discuss the need for an integrated software stack that unites artificial intelligence (AI) and modeling and simulation (ModSim) tools to advance scientific discovery.
Key challenges highlighted include balancing the distinct needs of AI and ModSim, especially in terms of software build practices, dependency management, and compatibility.
arXiv Detail & Related papers (2024-11-14T15:17:50Z) - Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI [116.8199519880327]
Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General Intelligence (AGI)<n>In this survey, we give a comprehensive exploration of the latest advancements in Embodied AI.
arXiv Detail & Related papers (2024-07-09T14:14:47Z) - Simulation Intelligence: Towards a New Generation of Scientific Methods [81.75565391122751]
"Nine Motifs of Simulation Intelligence" is a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system.
We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery.
arXiv Detail & Related papers (2021-12-06T18:45:31Z) - The Chef's Hat Simulation Environment for Reinforcement-Learning-Based
Agents [54.63186041942257]
We propose a virtual simulation environment that implements the Chef's Hat card game, designed to be used in Human-Robot Interaction scenarios.
This paper provides a controllable and reproducible scenario for reinforcement-learning algorithms.
arXiv Detail & Related papers (2020-03-12T15:52:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.