{"type":"ScholarlyArticle","title":"ScriptWorld: Text Based Environment for Learning Procedural Knowledge","authors":[{"name":"Abhinav Joshi","slug":"abhinav-joshi","given":"Abhinav","family":"Joshi","affiliation":"Indian Institute of Technology Kanpur (IIT-K)"},{"name":"Areeb Ahmad","slug":"areeb-ahmad","given":"Areeb","family":"Ahmad","affiliation":"Indian Institute of Technology Kanpur (IIT-K)"},{"name":"Umang Pandey","slug":"umang-pandey","given":"Umang","family":"Pandey","affiliation":"Indian Institute of Technology Kanpur (IIT-K)"},{"name":"Ashutosh Modi","slug":"ashutosh-modi","given":"Ashutosh","family":"Modi","affiliation":"Indian Institute of Technology Kanpur (IIT-K)"}],"abstract":"Text-based games provide a framework for developing natural language understanding and commonsense knowledge about the world in reinforcement learning based agents. Existing text-based environments often rely on fictional situations and characters to create a gaming framework and are far from real-world scenarios. In this paper, we introduce ScriptWorld: a text-based environment for teaching agents about real-world daily chores and hence imparting commonsense knowledge. To the best of our knowledge, it is the first interactive text-based gaming framework that consists of daily real-world human activities designed using scripts dataset. We provide gaming environments for 10 daily activities and perform a detailed analysis of the proposed environment. We develop RL-based baseline models/agents to play the games in ScriptWorld. To understand the role of language models in such environments, we leverage features obtained from pre-trained language models in the RL agents. Our experiments show that prior knowledge obtained from a pre-trained language model helps to solve real-world text-based gaming environments.","doi":"10.24963/ijcai.2023/566","url":"https://ijcai.org/proceedings/2023/566","pdf":"https://ijcai.org/proceedings/2023/0566.pdf","markdown":"https://ijcai.org/proceedings/2023/566.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/566","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Natural Language Processing","track":"Main Track","number":566,"pages":{"first":5095,"last":5103},"keywords":["Machine Learning: ML: Deep reinforcement learning","Agent-based and Multi-agent Systems: MAS: Applications","Natural Language Processing: NLP: Applications","Machine Learning: ML: Applications"],"videos":[],"references":[{"position":1,"raw":"[Adhikari et al., 2020] Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikulas Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, and William L. 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