SimDem A Multi-agent Simulation Environment to Model Persons with Dementia and their Assistance
Developing artificial intelligence based assistive systems to aid Persons with Dementia (PwD) requires large amounts of training data. However, data collection poses ethical, legal, economic, and logistic issues. Synthetic data generation tools, in this regard, provide a potential solution. However, we believe that already available such tools do not adequately reflect cognitive deficiencies in behavior simulation. To counter these issues we propose a simulation model (SimDem ) that primarily focuses on cognitive impairments suffered by PwD and can be easily configured and adapted by the users to model and evaluate assistive solutions.
Code (0)
등록된 구현이 없습니다.
Tasks
Synthetic Data GenerationSimilar Papers 제목 키워드 기반
An Agentic LLM Framework for Adverse Media Screening in AML Compliance
Adverse media screening is a critical component of anti-money laundering (AML) and know-your-customer (KYC) compliance processes in financial institutions. Traditional approaches rely on keyword-based searches that gener…
ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection
Detecting missing persons in forest environments remains a challenge, as dense canopy cover often conceals individuals from detection in top-down or oblique aerial imagery typically captured by Unmanned Aerial Vehicles (…
Object DetectionIntegrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care
This study explores a novel approach to advancing dementia care by integrating socially assistive robotics, reinforcement learning (RL), large language models (LLMs), and clinical domain expertise within a simulated envi…
Reinforcement Learning (RL)WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
Deep reinforcement learning (RL) is a powerful framework to train decision-making models in complex environments. However, RL can be slow as it requires repeated interaction with a simulation of the environment. In parti…
CPUDecision MakingDeep Reinforcement LearningGPU+3IndoorWorld: Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment
Virtual environments are essential to AI agent research. Existing environments for LLM agent research typically focus on either physical task solving or social simulation, with the former oversimplifying agent individual…
AI Agent