Project Sid: Many-agent simulations toward AI civilization
- URL: http://arxiv.org/abs/2411.00114v1
- Date: Thu, 31 Oct 2024 18:11:22 GMT
- Title: Project Sid: Many-agent simulations toward AI civilization
- Authors: Altera. AL, Andrew Ahn, Nic Becker, Stephanie Carroll, Nico Christie, Manuel Cortes, Arda Demirci, Melissa Du, Frankie Li, Shuying Luo, Peter Y Wang, Mathew Willows, Feitong Yang, Guangyu Robert Yang,
- Abstract summary: We demonstrate how 10 - 1000+ AI agents behave and progress within agent societies.
We first introduce the PIANO architecture, which enables agents to interact with humans and other agents in real-time.
We then evaluate agent performance in agent simulations using civilizational benchmarks inspired by human history.
- Score: 1.623086795799085
- License:
- Abstract: AI agents have been evaluated in isolation or within small groups, where interactions remain limited in scope and complexity. Large-scale simulations involving many autonomous agents -- reflecting the full spectrum of civilizational processes -- have yet to be explored. Here, we demonstrate how 10 - 1000+ AI agents behave and progress within agent societies. We first introduce the PIANO (Parallel Information Aggregation via Neural Orchestration) architecture, which enables agents to interact with humans and other agents in real-time while maintaining coherence across multiple output streams. We then evaluate agent performance in agent simulations using civilizational benchmarks inspired by human history. These simulations, set within a Minecraft environment, reveal that agents are capable of meaningful progress -- autonomously developing specialized roles, adhering to and changing collective rules, and engaging in cultural and religious transmission. These preliminary results show that agents can achieve significant milestones towards AI civilizations, opening new avenues for large simulations, agentic organizational intelligence, and integrating AI into human civilizations.
Related papers
- Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI [129.08019405056262]
Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial Intelligence (AGI)
MLMs andWMs have attracted significant attention due to their remarkable perception, interaction, and reasoning capabilities.
In this survey, we give a comprehensive exploration of the latest advancements in Embodied AI.
arXiv Detail & Related papers (2024-07-09T14:14:47Z) - A social path to human-like artificial intelligence [6.189233558074243]
We argue that the bottleneck in artificial intelligence (AI) progress is shifting from data assimilation to novel data generation.
We show that natural intelligence emerges at multiple scales in networks of interacting agents via collective living, social relationships and major evolutionary transitions.
arXiv Detail & Related papers (2024-05-22T15:38:10Z) - MineLand: Simulating Large-Scale Multi-Agent Interactions with Limited Multimodal Senses and Physical Needs [12.987019067098414]
We propose a multi-agent Minecraft simulator, MineLand, that bridges this gap by introducing three key features: large-scale scalability, limited multimodal senses, and physical needs.
Our simulator supports 64 or more agents. Agents have limited visual, auditory, and environmental awareness, forcing them to actively communicate and collaborate to fulfill physical needs like food and resources.
Our experiments demonstrate that the simulator, the corresponding benchmark, and the AI agent framework contribute to more ecological and nuanced collective behavior.
arXiv Detail & Related papers (2024-03-28T09:53:41Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - 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) - The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI)
We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents.
We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
arXiv Detail & Related papers (2023-09-14T17:12:03Z) - Generative Agents: Interactive Simulacra of Human Behavior [86.1026716646289]
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.
arXiv Detail & Related papers (2023-04-07T01:55:19Z) - 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) - Watch-And-Help: A Challenge for Social Perception and Human-AI
Collaboration [116.28433607265573]
We introduce Watch-And-Help (WAH), a challenge for testing social intelligence in AI agents.
In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently.
We build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines.
arXiv Detail & Related papers (2020-10-19T21:48:31Z)
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.