ScriptWorld: Text Based Environment for Learning Procedural Knowledge Article Swipe
Abhinav Joshi
,
Areeb Ahmad
,
Umang Pandey
,
Ashutosh Modi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2023/566
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2023/566
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/ijcai.2023/566
- https://www.ijcai.org/proceedings/2023/0566.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385768029
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385768029Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24963/ijcai.2023/566Digital Object Identifier
- Title
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ScriptWorld: Text Based Environment for Learning Procedural KnowledgeWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-08-01Full publication date if available
- Authors
-
Abhinav Joshi, Areeb Ahmad, Umang Pandey, Ashutosh ModiList of authors in order
- Landing page
-
https://doi.org/10.24963/ijcai.2023/566Publisher landing page
- PDF URL
-
https://www.ijcai.org/proceedings/2023/0566.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.ijcai.org/proceedings/2023/0566.pdfDirect OA link when available
- Concepts
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Scripting language, Computer science, Leverage (statistics), Reinforcement learning, Commonsense reasoning, Artificial intelligence, Commonsense knowledge, Natural language, Natural language understanding, Human–computer interaction, Natural language processing, Knowledge extraction, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Text-based | 0 |
| abstract_inverted_index.activities | 83, 95 |
| abstract_inverted_index.characters | 30 |
| abstract_inverted_index.developing | 6 |
| abstract_inverted_index.knowledge, | 68 |
| abstract_inverted_index.knowledge. | 62 |
| abstract_inverted_index.real-world | 40, 55, 81, 153 |
| abstract_inverted_index.scenarios. | 41 |
| abstract_inverted_index.situations | 28 |
| abstract_inverted_index.text-based | 22, 49, 74, 154 |
| abstract_inverted_index.understand | 117 |
| abstract_inverted_index.commonsense | 11, 61 |
| abstract_inverted_index.environment | 50 |
| abstract_inverted_index.experiments | 139 |
| abstract_inverted_index.interactive | 73 |
| abstract_inverted_index.pre-trained | 131, 147 |
| abstract_inverted_index.ScriptWorld. | 115 |
| abstract_inverted_index.ScriptWorld: | 47 |
| abstract_inverted_index.environment. | 104 |
| abstract_inverted_index.environments | 23, 91 |
| abstract_inverted_index.environments, | 125 |
| abstract_inverted_index.environments. | 156 |
| abstract_inverted_index.models/agents | 109 |
| abstract_inverted_index.reinforcement | 17 |
| abstract_inverted_index.understanding | 9 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.67487571 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |