Redirecting to original paper in 30 seconds...

Click below to go immediately or wait for automatic redirect

arxiv_ai 92% Match Research Paper AI Researchers,Robotics Engineers,HCI Researchers,Game AI Developers 1 week ago

Partner Modelling Emerges in Recurrent Agents (But Only When It Matters)

reinforcement-learning › multi-agent
📄 Abstract

Abstract: Humans are remarkably adept at collaboration, able to infer the strengths and weaknesses of new partners in order to work successfully towards shared goals. To build AI systems with this capability, we must first understand its building blocks: does such flexibility require explicit, dedicated mechanisms for modelling others -- or can it emerge spontaneously from the pressures of open-ended cooperative interaction? To investigate this question, we train simple model-free RNN agents to collaborate with a population of diverse partners. Using the `Overcooked-AI' environment, we collect data from thousands of collaborative teams, and analyse agents' internal hidden states. Despite a lack of additional architectural features, inductive biases, or auxiliary objectives, the agents nevertheless develop structured internal representations of their partners' task abilities, enabling rapid adaptation and generalisation to novel collaborators. We investigated these internal models through probing techniques, and large-scale behavioural analysis. Notably, we find that structured partner modelling emerges when agents can influence partner behaviour by controlling task allocation. Our results show that partner modelling can arise spontaneously in model-free agents -- but only under environmental conditions that impose the right kind of social pressure.
Authors (8)
Ruaridh Mon-Williams
Max Taylor-Davies
Elizabeth Mieczkowski
Natalia Velez
Neil R. Bramley
Yanwei Wang
+2 more
Submitted
May 22, 2025
arXiv Category
cs.AI
arXiv PDF

Key Contributions

Demonstrates that partner modeling (inferring collaborators' task abilities) can emerge spontaneously in simple model-free RNN agents trained for open-ended cooperative interaction in the `Overcooked-AI` environment. Agents develop structured internal representations of partners, enabling rapid adaptation and generalization to novel collaborators without explicit modeling mechanisms.

Business Value

Crucial for developing AI systems that can effectively collaborate with humans and other AI agents in complex, dynamic environments, leading to more intuitive human-AI teams and efficient multi-agent coordination.

Paper Metadata

Innovation Type

Emergent Behavior Discovery

Deployment Feasibility

High feasibility, as it demonstrates emergent capabilities in relatively simple RNN agents, suggesting potential for practical deployment in collaborative systems.

Limitations Addressed

Addresses the question of whether explicit mechanisms are required for partner modeling in AI, or if it can emerge from the pressures of cooperative interaction. It shows emergence is possible even with simple agents.

Technical Tags

partner modelingrecurrent agentscollaborationcooperative interactionhidden statestask abilitiesadaptationgeneralizationmodel-free RNNsOvercooked-AI

Research Topics

Multi-Agent Reinforcement LearningCooperative AIHuman-AI CollaborationAI LearningCognitive Modeling

Methods & Architectures

Training model-free RNN agentsAnalysis of hidden statesData collection from collaborative teams Recurrent Neural Networks (RNNs)

Applications & Tasks

Human-Robot Collaboration Multi-Agent Systems Game AI Teamwork Simulation Inferring partner abilitiesAdapting to new collaboratorsEmergence of partner modeling without explicit mechanisms Collaborative task completionLearning to work with diverse partnersDeveloping adaptive AI agents

Datasets & Benchmarks

Benchmarks
Overcooked-AI

Related Fields

Artificial IntelligenceReinforcement LearningMulti-Agent SystemsCognitive ScienceHuman-Computer Interaction

Keywords

partner modelingrecurrent agentscollaborationcooperative AImulti-agent systemsemergent behaviorRNNOvercooked-AIadaptationgeneralizationhidden statestask abilities

Academic Context

#Multi-Agent Reinforcement Learning#Cooperative AI#Human-AI Collaboration#AI Learning#Cognitive Modeling

Commercial Potential

Potential Products
Collaborative AI assistantsAdaptive multi-agent systemsAI teammates for complex tasks
Target Industries
RoboticsGamingCustomer ServiceLogisticsHealthcare
Use Case Examples
Developing robots that can learn a human partner's preferences and skillsCreating AI teammates in games that adapt to player strategiesBuilding systems where multiple AI agents can dynamically adjust their roles based on team needs
Competitive Edge
Demonstrates that sophisticated partner modeling can emerge organically in simple agents, challenging the need for explicit, complex mechanisms and paving the way for more adaptable collaborative AI.

Resource Requirements

Compute Needs
Requires compute for training RNN agents over many interactions in the Overcooked-AI environment.
Data Requirements
Requires interaction data generated from playing the Overcooked-AI game with diverse partner agents.
Deployment Constraints
The emergent behavior might be sensitive to training conditions and environment complexity. Generalization to vastly different tasks or partners might still be a challenge.
Scalability
Scalability is relevant to training agents that can handle populations of diverse partners.
View Full Paper Back to Papers