Discovery-Probability Graph Planning for Embodied Agents in Tasks with Hidden Cooperation Requirements

Key points

• Embodied agents are increasingly capable of perceiving, planning, and acting autonomously in dynamic environments, demonstrating strong performance on complex cooperative tasks.1
• In real-world applications, however, hidden-cooperation tasks are more common: the need for cooperation only becomes apparent after an agent observes and interacts with task-related targets.1
• In this paper, we first present HiddenCoop, a benchmark for effectively evaluating agent cooperation in hidden-cooperation tasks.1
• At the core of DPGP is a discovery-probability graph that encodes the probability distribution over the number and locations of task-related targets, facilitating effective and efficient information exchange among agents.1

Sources (1)

• [1]Discovery-Probability Graph Planning for Embodied Agents in Tasks with Hidden Cooperation Requirements
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 01:20 PM

  We further propose Discovery-Probability Graph Planning (DPGP), a method for enabling effective agent cooperation in such tasks.

  Embodied agents are increasingly capable of perceiving, planning, and acting autonomously in dynamic environments, demonstrating strong performance on complex cooperative tasks.

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