Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming

• URL: http://arxiv.org/abs/2502.06976v1
• Date: Mon, 10 Feb 2025 19:16:20 GMT
• Title: Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming
• Authors: Upasana Biswas, Siddhant Bhambri, Subbarao Kambhampati,
• Abstract summary: We propose the concept of interdependence to measure how much agents rely on each other's actions to achieve the shared goal. We pair state-of-the-art agents trained through MARL for HAT, with learned human models for the the popular Overcooked domain, and evaluate the team performance for these human-agent teams.
• Score: 14.489157453882767
• License:
• Abstract: The long-standing research challenges of Human-AI Teaming(HAT) and Zero-shot Cooperation(ZSC) have been tackled by applying multi-agent reinforcement learning(MARL) to train an agent by optimizing the environment reward function and evaluating their performance through task performance metrics such as task reward. However, such evaluation focuses only on task completion, while being agnostic to `how' the two agents work with each other. Specifically, we are interested in understanding the cooperation arising within the team when trained agents are paired with humans. To formally address this problem, we propose the concept of interdependence to measure how much agents rely on each other's actions to achieve the shared goal, as a key metric for evaluating cooperation in human-agent teams. Towards this, we ground this concept through a symbolic formalism and define evaluation metrics that allow us to assess the degree of reliance between the agents' actions. We pair state-of-the-art agents trained through MARL for HAT, with learned human models for the the popular Overcooked domain, and evaluate the team performance for these human-agent teams. Our results demonstrate that trained agents are not able to induce cooperative behavior, reporting very low levels of interdependence across all the teams. We also report that teaming performance of a team is not necessarily correlated with the task reward.

Related papers

• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [51.452664740963066]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments. We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions. Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task [56.92961847155029]
  Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. Mutual Theory of Mind (MToM) arises when AI agents with ToM capability collaborate with humans. We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent.
  arXiv  Detail & Related papers  (2024-09-13T13:19:48Z)
• Large Language Model-based Human-Agent Collaboration for Complex Task Solving [94.3914058341565]
  We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving. We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
  arXiv  Detail & Related papers  (2024-02-20T11:03:36Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• Warmth and competence in human-agent cooperation [0.7237068561453082]
  Recent studies demonstrate that AI agents trained with deep reinforcement learning are capable of collaborating with humans. We train deep reinforcement learning agents in Coins, a two-player social dilemma. Participants' perceptions of warmth and competence predict their stated preferences for different agents.
  arXiv  Detail & Related papers  (2022-01-31T18:57:08Z)
• Resonating Minds -- Emergent Collaboration Through Hierarchical Active Inference [0.0]
  We investigate how efficient, automatic coordination processes at the level of mental states (intentions, goals) can lead to collaborative situated problem-solving. We present a model of hierarchical active inference for collaborative agents (HAICA) We show that belief resonance and active inference allow for quick and efficient agent coordination, and thus can serve as a building block for collaborative cognitive agents.
  arXiv  Detail & Related papers  (2021-12-02T13:23:44Z)
• ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind [18.85252946546942]
  Theory of Mind (ToM) builds socially intelligent agents who are able to communicate and cooperate effectively. We demonstrate the idea in two typical target-oriented multi-agent tasks: cooperative navigation and multi-sensor target coverage.
  arXiv  Detail & Related papers  (2021-10-15T18:29:55Z)
• 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)
• UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning [53.73686229912562]
  We propose a novel MARL approach called Universal Value Exploration (UneVEn) UneVEn learns a set of related tasks simultaneously with a linear decomposition of universal successor features. Empirical results on a set of exploration games, challenging cooperative predator-prey tasks requiring significant coordination among agents, and StarCraft II micromanagement benchmarks show that UneVEn can solve tasks where other state-of-the-art MARL methods fail.
  arXiv  Detail & Related papers  (2020-10-06T19:08:47Z)

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.