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MineLand: Simulating Large-Scale Multi-Agent Interactions with Limited Multimodal Senses and Physical Needs (2403.19267v2)

Published 28 Mar 2024 in cs.CL and cs.AI

Abstract: While Vision-LLMs (VLMs) hold promise for tasks requiring extensive collaboration, traditional multi-agent simulators have facilitated rich explorations of an interactive artificial society that reflects collective behavior. However, these existing simulators face significant limitations. Firstly, they struggle with handling large numbers of agents due to high resource demands. Secondly, they often assume agents possess perfect information and limitless capabilities, hindering the ecological validity of simulated social interactions. To bridge this gap, 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. Additionally, we further introduce an AI agent framework, Alex, inspired by multitasking theory, enabling agents to handle intricate coordination and scheduling. Our experiments demonstrate that the simulator, the corresponding benchmark, and the AI agent framework contribute to more ecological and nuanced collective behavior.The source code of MineLand and Alex is openly available at https://github.com/cocacola-lab/MineLand.

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Citations (4)
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Summary

  • The paper introduces MineLand, a simulator that models realistic social dynamics among up to 48 agents by integrating limited multimodal senses and essential physical needs.
  • It utilizes an enhanced Mineflayer-based architecture, enabling efficient, large-scale agent interactions with low computational overhead.
  • Empirical results demonstrate the simulator’s ability to reveal insights into coordinated behaviors and decision-making in multi-agent systems.

Exploring MineLand: A Novel Simulator for Large-Scale Multi-Agent Interactions with Limited Multimodal Senses

Introduction to MineLand Simulator

Recent advancements in AI research have significantly emphasized the creation of simulators to investigate complex behavior and social dynamics within artificial societies. MineLand positions itself distinctively in this arena by addressing the limitations associated with conventional multi-agent simulators. Designed to simulate intricate social interactions within a Minecraft-based environment, MineLand accommodates up to 48 agents, pushing the boundaries by emphasizing the ecological validity through introducing constraints on agents' multimodal senses and embedding physical necessities like food and shelter into their operational logic.

Architectural Overview

MineLand advantages stem from its innovative architecture, which enables the support of a high number of agents on standard computing hardware. The simulator is built upon an enhanced version of the popular Minecraft bot API, Mineflayer, to ensure both performance efficiency and extensive modularity. This architecture comprises bot, environment, and bridge modules that collectively facilitate large-scale agent interactions with minimal computational overhead.

Observation and State Spaces

Observation in MineLand is crafted to closely mimic human sensory limitations, offering agents a partially observable view of the environment through eco-centric visual, auditory, and tactile senses. The state space further introduces a novel aspect to agent-based simulators by integrating physical needs and daily routines into the agent model. These additions compel agents to make decisions that mirror human-like prioritization and societal interaction, including resource allocation, task coordination, and survival strategies.

Action Space and Communication

MineLand's action space is notably comprehensive, allowing for both low-level actions such as object manipulation and high-level strategic tasks like coordinated construction. The communication feature stands out by facilitating natural and dynamic interactions among agents, encouraging them to collaborate or compete efficiently within shared tasks and objectives.

MineLand Benchmark Suite

The Benchmark Suite is a versatile toolkit within MineLand, offering a wide range of tasks from simple resource harvesting to complex construction and survival scenarios. It serves as a rigorous testing ground for evaluating and benchmarking the emergent behaviors and efficiency of multi-agent collaborations within the simulated environment.

Implementing the Alex AI Framework

Developed alongside MineLand is the Alex AI agent framework, inspired by multitasking theory. Alex showcases the ability of agents to not only navigate the rich and constrained environment of MineLand but also to engage in complex scheduling and coordination tasks. The framework particularly shines in scenarios requiring agents to balance between their limited sensory inputs, physical needs, and the execution of multifaceted tasks.

Empirical Insights

Through a series of experiments, MineLand and the Alex framework demonstrated significant potential in driving forward the understanding of multi-agent systems. Notably, agents exhibited enhanced performance in tasks requiring active communication and cooperation, underscored by the role of limited senses and physical needs in fostering realistic agent behaviors.

Theoretical and Practical Implications

MineLand opens new avenues for exploring the dynamics of large-scale multi-agent systems within ecologically valid settings. Its emphasis on limited senses, physical needs, and large agent populations provides invaluable insights into naturalistic agent behaviors and social interactions. This can extend to diverse domains such as robotics, game design, and social behavior modeling, offering a rich playground for both theoretical exploration and practical application.

Future Directions in AI research

Given its foundational approach and robust architecture, MineLand sets the stage for future explorations into more complex and nuanced multi-agent interactions. Potential developments could see the integration of more sophisticated cognitive models and decision-making algorithms, further bridging the gap between artificial and naturalistic intelligence systems.

In summary, MineLand represents a significant leap towards creating more realistic and dynamic simulations of large-scale multi-agent systems. By emphasizing ecological validity through limited senses, physical needs, and a flexible interaction framework, it paves the way for new discoveries in AI and multi-agent collaborative behaviors.

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