Fact Probability Vector Based Goal Recognition

Nils Wilken, Lea Cohausz, Christian Bartelt, Heiner Stuckenschmidt
8/26/2024
cs.AI

No official code found. Best implementation: nilsWilken/Fact-Observation-Probability-Based-Goal-Recognition_ECAI24 (9 stars, MIT, last commit 2025-05-23).

Abstract

We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities. These probabilities depend on a specified goal g and initial state s0. Our method maps these probabilities and observed facts into a real vector space to compute heuristic values for potential goals. These values estimate the likelihood of a given goal being the true objective of the observed agent. As obtaining exact expected probabilities for observed facts in an observation sequence is often practically infeasible, we propose and empirically validate a method for approximating these probabilities. Our empirical results show that the proposed approach offers improved goal recognition precision compared to state-of-the-art techniques while reducing computational complexity.

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Code Implementations(1)

nilsWilken/Fact-Observation-Probability-Based-Goal-Recognition_ECAI24Community100%
MIT

This repository contains the code used for the experiments for the publication "Fact Probability Vector Based Goal Recognition" which will be presented at ECAI 2024

90TeX, JavaAug 15, 20241 years agoMIT

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Cite this paper

@article{wilken2024fact,
  title  = {Fact Probability Vector Based Goal Recognition},
  author = {Nils Wilken and Lea Cohausz and Christian Bartelt and Heiner Stuckenschmidt},
  year   = {2024},
  eprint = {2408.14224},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.14224v1}
}

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