---
title: "Player Goal Recognition in Open-World Digital Games with Long Short-Term Memory Networks"
authors:
  - "Wookhee Min"
  - "Bradford W. Mott"
  - "Jonathan P. Rowe"
  - "Barry Liu"
  - "James C. Lester"
doi: ""
url: "https://ijcai.org/proceedings/2016/368"
pdf: "https://ijcai.org/proceedings/2016/0368.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "2590-2595"
track: ""
keywords:
---

# Player Goal Recognition in Open-World Digital Games with Long Short-Term Memory Networks

Wookhee Min, Bradford W. Mott, Jonathan P. Rowe, Barry Liu, James C. Lester

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 2590-2595.

## Abstract

Recent years have seen a growing interest in player modeling for digital games. Goal recognition, which aims to accurately recognize players' goals from observations of low-level player actions, is a key problem in player modeling. However, player goal recognition poses significant challenges because of the inherent complexity and uncertainty pervading gameplay. In this paper, we formulate player goal recognition as a sequence labeling task and introduce a goal recognition framework based on long short-term memory (LSTM) networks. Results show that LSTM-based goal recognition is significantly more accurate than previous state-of-the-art methods, including n-gram encoded feedforward neural networks pre-trained with stacked denoising autoencoders, as well as Markov logic network-based models. Because of increased goal recognition accuracy and the elimination of labor-intensive feature engineering, LSTM-based goal recognition provides an effective solution to a central problem in player modeling for open-world digital games.