Humanoid Agents: Platform for Simulating Human-like Generative Agents (2023)

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

## introduction ushered in by the landmark paper on generative agents @xcite , the promise of modelling conceivable human behavior using advanced nlp systems has sparked the imagination of many. generative agents plan activities over a day, execute them at each time-step and adapt their plans based on observations of their environment. while this approach can generate seemingly believe-able activities to external observers, this process does not fully resemble how humans think. most of us do not create plans well in advance, and then meticulously and precisely carry out those plans in day-to-day life. instead, we constantly adapt our * equal contribution plans to how we feel on the inside, in addition to changes in our physical environment. to mitigate this shortcoming, we draw inspiration from psychology to propose humanoid agents. @xcite suggests that humans have two complementary processes for thinking: system 1 is intuitive, effortless and instantaneous while system 2 is logical, intentional and slow. generative agents focus on system 2 thinking at the cost of system 1. to better guide the behavior of humanoid agents using system 1, we introduce three aspects of system 1 that can influence their behavior: basic needs, emotion and the closeness of their social relationship with other agents. basic needs refer to intrinsic needs that humans have for survival @xcite . to appropriately model human behavior, agents need to interact with others, maintain their health and rest. failing to do so adequately, agents will receive negative feedback comprising loneliness, sickness and tiredness, as illustrated in fig. 1 . system 2 planning alone can implicitly account for activities to meet these needs (e.g. planning time for rest) but without feedback from system 1, agents cannot adapt by having a nap at 3pm if tired but bedtime is planned for midnight. similarly, a realistic model of human behavior needs to consider the agents' emotions @xcite . if an agent is feeling angry, it should be able to respond by doing something that helps it to vent its emotion, such as going for a run or doing meditation. the relationship closeness of an agent to other agents should also influence how they engage with other agents. the social brain hypothesis proposes that a large part of our cognitive ability evolved to track the quality of social relationships @xcite , which means that people often adapt their interactions with others based on how close they feel to them @xcite . to better imitate humans, we empower humanoid agents to adapt their conversations with one another based on how close they are to one another. we present a platform that can simulate the behavior of humanoid agents in various settings (three of which we demonstrate), visualize them using an unity webgl game interface and present the statuses of simulated agents over time using an interactive analytics dashboard. we then show experiments that validate how humanoid agents effectively respond to and infer changes in each aspect of system 1. while our paper demonstrates how three different aspects of system 1 influence agent behavior, our system is also extensible to many more aspects, such as personality @xcite , moral values @xcite , empathy @xcite , helpfulness @xcite , cultural background @xcite and other personal attributes @xcite . ## humanoid agents illustrated in fig. 2 , the architecture of humanoid agents is based on generative agents with improvement to agent initialization, activity planning and dialogue generation in order to account for system 1 thinking processes. akin to generative agents, chatgpt 3.5 is used for all generations, but support for other language models is planned for future development. ## platform implementation in this section, we introduce how we build worlds and model the passage of time on our platform. then, we describe how our unity webgl game interface and interactive analytics dashboard can help users to visualize agent statuses. ## experiments we first investigate the effectiveness of humanoid agents in evaluating the effects of activities and conversations have on various aspects of system 1, by comparing its predictions with human annotations. then, we study the effects that system 1 attributes have on activities and conversations. due to page length limitations, we only present the effects of basic needs on activities, and leave the effects of emotions and relationship closeness to appendices a.1 and a.2 respectively. ## conclusion we propose humanoid agents, a platform for human-like simulations of generative agents guided by system 1 processing including basic needs (e.g. hunger, health and energy), emotion and closeness in relationships. our platform also powers the immersive visualization of agents using our unity webgl game interface and an interactive analytics dashboard.