# An LP-Based Approach for Goal Recognition as Planning

## TL;DR

This paper introduces an LP-based method for goal recognition that explicitly handles partial and noisy observations, improving accuracy and reliability over previous approaches.

## Contribution

The paper presents a novel operator-counting framework for goal recognition that effectively manages uncertainty and sensor unreliability in planning tasks.

## Key findings

- Outperforms previous methods in accuracy and agreement ratio
- Effectively handles partial and noisy observations
- Provides a foundation for future combinatorial optimization research in goal recognition

## Abstract

Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent. In this paper, we develop a method based on the operator-counting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recognition tasks. Our method reasons explicitly about both partial and noisy observations: estimating uncertainty for the former, and satisfying observations given the unreliability of the sensor for the latter. We evaluate our approach empirically over a large data set, analyzing its components on how each can impact the quality of the solutions. In general, our approach is superior to previous methods in terms of agreement ratio, accuracy, and spread. Finally, our approach paves the way for new research on combinatorial optimization to solve goal recognition tasks.