Human Behavior Simulation: Objectives, Methodologies, and Open Problems
November 26, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Zhang Guozhen, Yu Zihan, Li Nian, Yu Fudan, Long Qingyue, Jin Depeng, Li Yong
arXiv ID
2412.07788
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In recent years, human behavior simulation has drawn increasing attention from both academia and industry. The reasons fall into two aspects. First, simulation serves as a critical tool for understanding human behaviors, which has become one of the most important research topics in the history. Second, researchers have gradually reached a consensus that simulation, especially human behavior simulation, is critical for real-world decision-making systems. As a result, lots of human behavior simulation research and applications have sprung up across numerous disciplines in the past few years. In addition to the traditional methods, such as building mathematical and physical models, leveraging the recent advances of deep learning techniques -- especially the nascent Large Language Model technology -- for accurate human behavior simulation has also been one of the hottest research topics. In this study, we provide a comprehensive review of the latest research advancements in human behavior simulation. We summarize the objectives, problem formulations, and commonly used methods and discuss the consistency in the development of related research in different disciplines, which reveals the gaps and opportunities for high-impact research in this promising direction.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted