# Affordance Extraction with an External Knowledge Database for Text-Based Simulated Environments

arXiv: https://arxiv.org/abs/2207.00265 · HTML: https://mlsanity.com/paper/2207.00265/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 32 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 20 of 20)

- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Graph Constrained Reinforcement Learning for Natural Language Action Spaces](https://mlsanity.com/paper/2001.08837/) (2020)
- [Investigating Human Priors for Playing Video Games](https://mlsanity.com/paper/1802.10217/) (2018)
- [What can I do here? A Theory of Affordances in Reinforcement Learning](https://mlsanity.com/paper/2006.15085/) (2020)
- [Exploration Based Language Learning for Text-Based Games](https://mlsanity.com/paper/2001.08868/) (2020)
- [NAIL: A General Interactive Fiction Agent](https://mlsanity.com/paper/1902.04259/) (2019)
- [LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games](https://mlsanity.com/paper/1909.01646/) (2019)
- [Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge](https://mlsanity.com/paper/2005.00811/) (2020)
- [Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction](https://mlsanity.com/paper/1911.12511/) (2019)
- [What can you do with a rock? Affordance extraction via word embeddings](https://mlsanity.com/paper/1703.03429/) (2017)
- [Comprehensible Context-driven Text Game Playing](https://mlsanity.com/paper/1905.02265/) (2019)
- [How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure Agents](https://mlsanity.com/paper/2002.08795/) (2020)
- [Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces](https://mlsanity.com/paper/1905.09700/) (2019)
- [The Text-Based Adventure AI Competition](https://mlsanity.com/paper/1808.01262/) (2018)
- [Towards Solving Text-based Games by Producing Adaptive Action Spaces](https://mlsanity.com/paper/1812.00855/) (2018)
- [Building Dynamic Knowledge Graphs from Text-based Games](https://mlsanity.com/paper/1910.09532/) (2019)
- [Affordance Extraction and Inference based on Semantic Role Labeling](https://mlsanity.com/paper/1809.00589/) (2018)
- [Baselines for Reinforcement Learning in Text Games](https://mlsanity.com/paper/1811.02872/) (2018)

## Built on by (most cited first, 1 of 1)

- [Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games](https://mlsanity.com/paper/2312.04657/) (2023)

## Abstract (arXiv)

Text-based simulated environments have proven to be a valid testbed for machine learning approaches. The process of affordance extraction can be used to generate possible actions for interaction within such an environment. In this paper the capabilities and challenges for utilizing external knowledge databases (in particular ConceptNet) in the process of affordance extraction are studied. An algorithm for automated affordance extraction is introduced and evaluated on the Interactive Fiction (IF) platforms TextWorld and Jericho. For this purpose, the collected affordances are translated into text commands for IF agents. To probe the quality of the automated evaluation process, an additional human baseline study is conducted. The paper illustrates that, despite some challenges, external databases can in principle be used for affordance extraction. The paper concludes with recommendations for further modification and improvement of the process.