Generative Agents: Interactive Simulacra of Human Behavior

• URL: http://arxiv.org/abs/2304.03442v2
• Date: Sun, 6 Aug 2023 00:21:19 GMT
• Title: Generative Agents: Interactive Simulacra of Human Behavior
• Authors: Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein
• Abstract summary: We introduce generative agents--computational software agents that simulate believable human behavior. We describe an architecture that extends a large language model to store a complete record of the agent's experiences. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims.
• Score: 86.1026716646289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.

Related papers

• Generative Agent Simulations of 1,000 People [56.82159813294894]
  We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals. The generative agents replicate participants' responses on the General Social Survey 85% as accurately as participants replicate their own answers. Our architecture reduces accuracy biases across racial and ideological groups compared to agents given demographic descriptions.
  arXiv  Detail & Related papers  (2024-11-15T11:14:34Z)
• Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities [0.0]
  We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. By analyzing this multi-agent simulation, we report valuable new insights into how social norms, cooperation, and personality traits can emerge spontaneously.
  arXiv  Detail & Related papers  (2024-11-05T16:49:33Z)
• Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
  "Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data. We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
  arXiv  Detail & Related papers  (2024-01-07T19:11:18Z)
• AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems [112.76941157194544]
  We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
  arXiv  Detail & Related papers  (2023-10-13T16:37:14Z)
• Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning [20.02604302565522]
  A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language. Here we study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. We show that imitation learning of human-human interactions in a simulated world, in conjunction with self-supervised learning, is sufficient to produce a multimodal interactive agent, which we call MIA, that successfully interacts with non-adversarial humans 75% of the time.
  arXiv  Detail & Related papers  (2021-12-07T15:17:27Z)
• Towards Socially Intelligent Agents with Mental State Transition and Human Utility [97.01430011496576]
  We propose to incorporate a mental state and utility model into dialogue agents. The hybrid mental state extracts information from both the dialogue and event observations. The utility model is a ranking model that learns human preferences from a crowd-sourced social commonsense dataset.
  arXiv  Detail & Related papers  (2021-03-12T00:06:51Z)
• Imitating Interactive Intelligence [24.95842455898523]
  We study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. To build agents that can robustly interact with humans, we would ideally train them while they interact with humans. We use ideas from inverse reinforcement learning to reduce the disparities between human-human and agent-agent interactive behaviour.
  arXiv  Detail & Related papers  (2020-12-10T13:55:47Z)
• SPA: Verbal Interactions between Agents and Avatars in Shared Virtual Environments using Propositional Planning [61.335252950832256]
  Sense-Plan-Ask, or SPA, generates plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments. We find that our algorithm creates a small runtime cost and enables agents to complete their goals more effectively than agents without the ability to leverage natural-language communication.
  arXiv  Detail & Related papers  (2020-02-08T23:15:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.