Fact Probability Vector Based Goal Recognition
This work addresses goal recognition in AI planning, offering incremental improvements in efficiency and accuracy for applications like autonomous systems or user intent prediction.
The paper tackles goal recognition by comparing observed facts with their expected probabilities under potential goals, proposing an approximation method for these probabilities. Empirical results show improved precision and reduced computational complexity compared to state-of-the-art techniques.
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.