RAPID: A Belief Convergence Strategy For Adaptive Multiagent Collaboration.pdf

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Preview of RAPID: A Belief Convergence Strategy for Adaptive Multiagent Collaboration
🔗 Source: cdn.aaai.org
📊 Size: 984 KB
👤 Author: Trevor Sarratt, Arnav Jhala
⬇️ Downloads: 44

Summary

Problem: In dynamic environments, assuming a teammate sticks to one goal is unrealistic. Existing belief update methods struggle to adapt quickly when teammates change their behaviors.

Solution: RAPID

RAPID differs from traditional approaches by:

- Maintaining Belief Distribution: Instead of sharply separating current task weights from others after each action, RAPID keeps potential alternative goals at relevant weights.
- Responsive Adaptation: It identifies changes in teammate behavior and adapts planning accordingly when enough evidence supports a switch in goals.

Methodology:

1. Partially Observable Markov Decision Process (POMDP): The problem is modeled as a POMDP, allowing for uncertainty about the environment and observations.
2. Monte-Carlo Tree Search (MCTS): RAPID uses MCTS, an efficient algorithm for exploring action policies in large state spaces.
3. Belief Revision: Beliefs are updated based on observed behavior, allowing for a more flexible understanding of teammate goals.

Evaluation:

The approach is evaluated using the Cops and Robbers domain. RAPID demonstrates superior performance compared to traditional belief revision methods, especially when dealing with non-static teammate behaviors.

Applications & Extensions:

RAPID's adaptability makes it suitable for:

- Human-agent collaboration
- Ad hoc multiagent teams
- Situations requiring rapid goal adaptation

Future work includes exploring more complex scenarios and integrating learning mechanisms to further enhance RAPID's capabilities.

Description

RAPID is a strategy that enables agents to quickly update their beliefs about other agents' goals in collaborative multiagent planning, even when those goals change unpredictably, improving performance over traditional methods.

Technical Information

  • File Format: PDF
  • File Size: 984 KB
  • Pages: 6
  • Language: EN
  • Author: Trevor Sarratt, Arnav Jhala
  • Total Downloads: 44
  • Last Updated: 11 hours ago

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