Loading Now

Summary of Beyond Static Assumptions: the Predictive Justified Perspective Model For Epistemic Planning, by Weijia Li et al.


Beyond Static Assumptions: the Predictive Justified Perspective Model for Epistemic Planning

by Weijia Li, Guang Hu, Yangmengfei Xu

First submitted to arxiv on: 10 Dec 2024

Categories

  • Main: Artificial Intelligence (cs.AI)
  • Secondary: None

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper tackles Epistemic Planning (EP), a crucial research area focused on understanding knowledge and beliefs among agents in cooperative or competitive environments. The Justified Perspective (JP) model is currently the leading approach to solving EP efficiently and effectively. However, existing EP methods rely on the static environment assumption, which hinders their application in fields like robotics with dynamic settings.
Low GrooveSquid.com (original content) Low Difficulty Summary
EP helps agents reason about each other’s knowledge and beliefs, making it essential for cooperative or competitive scenarios. The JP model is a state-of-the-art approach that solves EP problems efficiently and effectively. However, existing methods assume the environment remains static, which limits their application in fields like robotics where variables change.

Keywords

» Artificial intelligence