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Source note2 min read

Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound

Source

arXiv

arxiv.org/abs/2606.25978v1

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Source

Plain-Language Brief

Researchers propose a method called Multi-Agent Goal Recognition with Branch-and-Bound (MAGR-BB) to infer the goals of multiple agents in real applications like drone surveillance and collaborative robotics, where only agents' trajectory is visible. MAGR-BB uses a shared team- and goal-conditioned policy as a scoring model in a factorized branch-and-bound search. This method outperforms exhaustive search in terms of reducing hypothesis materialization by orders of magnitude and recognizing goals faster on a controlled multi-agent Blocksworld benchmark.

What the Document Says

  • Multi-Agent Goal Recognition with Branch-and-Bound (MAGR-BB) is a method to infer the goals of multiple agents in applications where only their trajectory is visible.
  • The method uses a shared team- and goal-conditioned policy as a scoring model inside a factorized branch-and-bound search.
  • MAGR-BB performs similarly to exhaustive search in terms of identifying the correct hypothesis throughout the trajectory.
  • It significantly reduces the amount of hypothesis materialization and cuts down on cumulative recognition runtime on a controlled multi-agent Blocksworld benchmark.

Key Concepts Mentioned

  • Multi-Agent Goal Recognition
  • Branch-and-Bound
  • Team-Conditioned Policy
  • Goal-Conditioned Policy
  • Factorized Branch-and-Bound Search
  • Exhaustive Search
  • Trajectory

Claims Made by the Document

  • MAGR-BB can infer the goals of multiple agents in real applications.
  • MAGR-BB outperforms exhaustive search in terms of reducing hypothesis materialization and recognition runtime.

Evidence Used

  • The paper presents a method called Multi-Agent Goal Recognition with Branch-and-Bound (MAGR-BB) which addresses the setting of inferring goals from agents' trajectory alone.
  • The performance of MAGR-BB is compared to exhaustive search on a controlled multi-agent Blocksworld benchmark, where it performs similarly in terms of identifying the correct hypothesis and significantly reduces hypothesis materialization and cumulative recognition runtime.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Multi-agent goal recognition asks an observer to jointly infer which agents act together and what each team is trying to achieve, so the hypothesis space grows combinatorially with the number of team partitions and goals per team. Real applications such as drone surveillance and collaborative robotics expose only the agents' trajectory, which forces the observer to rank team-goal hypotheses from behavior alone. Multi-Agent Goal Recognition with Branch-and-Bound (MAGR-BB) addresses this setting with a shared team- and goal-conditioned policy used as the scoring model inside a factorized branch-and-bound search. On a controlled multi-agent Blocksworld benchmark, MAGR-BB returns the same top-ranked hypothesis as exhaustive search throughout the trajectory while cutting hypothesis materialization by orders of magnitude and reducing cumulative recognition runtime substantially.