Humanoid Agents: Platform for Simulating Human-like Generative Agents

Abstract: Just as computational simulations of atoms, molecules and cells have shaped the way we study the sciences, true-to-life simulations of human-like agents can be valuable tools for studying human behavior. We propose Humanoid Agents, a system that guides Generative Agents to behave more like humans by introducing three elements of System 1 processing: Basic needs (e.g. hunger, health and energy), Emotion and Closeness in Relationships. Humanoid Agents are able to use these dynamic elements to adapt their daily activities and conversations with other agents, as supported with empirical experiments. Our system is designed to be extensible to various settings, three of which we demonstrate, as well as to other elements influencing human behavior (e.g. empathy, moral values and cultural background). Our platform also includes a Unity WebGL game interface for visualization and an interactive analytics dashboard to show agent statuses over time. Our platform is available on https://www.humanoidagents.com/ and code is on https://github.com/HumanoidAgents/HumanoidAgents

• The paper introduces a novel platform that integrates System 1 processes with emotional and need-based dynamics to simulate human-like behavior.
• It employs a dual-framework architecture combining fast intuitive reactions and deliberate planning to replicate real-world social interactions.
• Experiments validated its efficacy using Unity WebGL visualizations and analytics dashboards, showing reliable responses in fullness, energy, and emotional states.

Humanoid Agents: A Platform for Simulating Human-like Generative Agents

This essay summarizes the paper "Humanoid Agents: Platform for Simulating Human-like Generative Agents" (2310.05418), which introduces a novel framework for creating and simulating human-like agents using System 1 processing principles. These principles integrate basic human needs, emotions, and the closeness of relationships into digital agents. The platform provides an innovative approach to simulate human behavior with applications across diverse domains, offering tools for visualizing agent dynamics and interactions.

Motivation and Objectives

The paper argues that conventional generative agents primarily focus on System 2 thinking—deliberative and slow processes. However, real human behavior, as posited by Kahneman's theory, involves dynamic interactions between System 1 (intuitive, fast) and System 2 processes. Humanoid Agents aim to bridge this gap by incorporating System 1 elements, such as basic needs and emotions, into the simulation of human-like agents that adapt their activities and interactions akin to humans in real life.

Architecture and Implementation

Humanoid Agents employ an architecture that seamlessly integrates System 1 processes alongside existing System 2 frameworks in generative agents.

Figure 1: Humanoid Agents are guided by both System 1 thinking to respond to their embodied conditions such as their basic needs and System 2 thinking involving explicit planning.

The architecture initiates agents with attributes that include age, personality traits, and default emotions. Agents react to internal state changes—like feeling hungry or exhausted—and adjust their activities accordingly, supported by dynamic elements such as emotions and relationship closeness.

Figure 2: Architecture of Humanoid Agents. Step 1: Agent is initialized based on user-provided seed information.

The platform utilizes a combination of Unity WebGL for visualization and interactive analytics dashboards to track basic needs fulfillment and social interactions over time, providing a comprehensive view of agent activities in simulated environments.

Figure 3: Unity WebGL Game Interface for visualizing Humanoid Agents situated in their world.

Figure 4: Interactive Analytics Dashboard for visualizing basic needs satisfaction of Humanoid Agent over time.

Experiments and Validation

Experiments were conducted to evaluate how well Humanoid Agents can predict the effects of activities and interactions on System 1 attributes. Comparisons with human annotations showed reliable performance, especially in identifying changes in fullness and energy levels, emotional states, and closeness from dialogues. These experiments underscore the platform's efficacy in simulating complex human-like social interactions.

Application and Future Prospects

The Humanoid Agents platform is highly extensible, supporting additional attributes such as empathy, personality, and cultural background, augmenting its versatility in simulating a broad spectrum of human behaviors. Its ability to simulate engaging and responsive environments can significantly benefit fields like computational social science, psychology, and human-computer interaction research.

Conclusion

The Humanoid Agents platform provides a robust methodology for simulating human-like agents, incorporating nuanced aspects of human psychology (System 1 processes) into generative systems. This approach enhances the fidelity of agent simulations, aligning closer to real-world human interactions and adaptable behaviors. The platform holds the promise of enriching simulations with deep insights into human-like generative modeling, fostering advancements across diverse computational domains.