**Generative Agents: Interactive Simulacra of Human Behavior** ![[genAgentBeh.png]] https://arxiv.org/abs/2304.03442 **What is most interesting here is the model of human behavior, or what they call 'generative agent architecture'.** ![[genAgentArch.png]] **GPT4 says this:** This paper presents the concept of generative agents, which are AI-driven interactive simulacra of human behavior. The aim of these agents is to model and simulate human behavior in various domains, such as social interactions, economic decisions, and problem-solving, among others. The paper is organized into the following sections: 1. Introduction: The authors provide an overview of the importance of understanding and simulating human behavior, as well as the potential applications of generative agents in various fields, including psychology, sociology, economics, and artificial intelligence. 2. Background and Related Work: This section reviews the previous work on modeling human behavior and the current state of the art in AI-generated behavior simulations. The authors discuss the limitations of existing approaches and propose generative agents as a novel solution. 3. Generative Agents Framework: The authors present the theoretical foundation and architecture of generative agents, which consist of several interconnected modules, such as perception, cognition, decision-making, and action. These modules work together to produce realistic and interactive human-like behavior. 4. Implementation and Evaluation: This section describes the implementation of generative agents in a variety of scenarios, such as social interactions, economic transactions, and problem-solving tasks. The authors also present a series of experiments to evaluate the performance and effectiveness of the proposed generative agents in simulating human behavior. 5. Applications: The paper discusses potential applications of generative agents in various domains, including virtual reality, video games, robotics, and training simulations. The authors highlight the importance of generative agents in creating more immersive and engaging experiences in these fields. 6. Limitations and Future Work: The authors acknowledge the limitations of the current generative agents framework, such as the need for more accurate human behavior models and the challenge of scalability. They propose future research directions to address these limitations and improve the performance of generative agents. 7. Conclusion: The paper concludes by emphasizing the potential of generative agents in advancing our understanding of human behavior and creating more realistic and interactive simulations. The authors encourage further research and development in this area to unlock the full potential of generative agents as a powerful tool for understanding, predicting, and influencing human behavior. **A crucial concept here is this one:** Believable Proxies of Human Behavior Believable agents are a key goal in the development of artificial intelligence systems that simulate human behavior. These agents are designed to create an illusion of life, making them appear realistic in their decision-making and actions, just like characters in Disney movies. By doing so, they can become believable proxies, or convincing stand-ins, for human behavior in simulated environments. The primary purpose of believable agents is to engage in social interactions with users or other agents in a way that convincingly mimics real human behavior. This can lead to emergent, or spontaneously arising, behaviors and interactions that make the simulation feel more genuine. Historically, these agents were developed for use in intelligent non-player characters (NPCs) in video games and interactive fiction. Creating NPCs with believable behavior can greatly enhance player experiences by allowing for emergent narratives and more engaging social interactions with the agents. But the significance of believable agents extends beyond just video games. As game worlds increasingly resemble real-world environments, they provide a valuable testing ground for AI developers. In these simulated worlds, developers can focus on refining the cognitive capabilities of believable agents without the challenges of implementing robotics in the real world or building simulation environments from scratch. As noted by Laird and van Lent in 2001, these game worlds offer an accessible testbed for developing and improving believable agents, making them even more convincing as proxies for human behavior in various simulations, including those that model individuals and communities. **I think once the behavior is solved, they will have to move into the problem of 'expression'.**