Learning to Generate Natural Language Rationales for Game Playing Agents Article Swipe
Upol Ehsan
,
Pradyumna Tambwekar
,
Larry Chan
,
Brent Harrison
,
Mark Riedl
·
YOU?
·
· 2018
· Open Access
·
YOU?
·
· 2018
· Open Access
·
Many computer games feature non-player charactert (NPC) teammates and companions; however, playing with or against NPCs can be frustrating when they perform unexpectedly. These frustrations can be avoided if the NPC has the ability to explain its actions and motivations. When NPC behavior is controlled by a black box AI system it can be hard to generate the necessary explanations. In this paper, we present a system that generates human-like, natural language explanations—called rationales—of an agent's actions in a game environment regardless of how the decisions are made by a black box AI. We outline a robust data collection and neural network training pipeline that can be used to gather think-aloud data and train a rationale generation model for any similar sequential turn based decision making task. A human-subject study shows that our technique produces believable rationales for an agent playing the game, Frogger. We conclude with insights about how people perceive automatically generated rationales.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://uknowledge.uky.edu/cs_facpub/18
- https://uknowledge.uky.edu/cs_facpub/18
- OA Status
- green
- Cited By
- 2
- References
- 10
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2913958482
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W2913958482Canonical identifier for this work in OpenAlex
- Title
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Learning to Generate Natural Language Rationales for Game Playing AgentsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-01-01Full publication date if available
- Authors
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Upol Ehsan, Pradyumna Tambwekar, Larry Chan, Brent Harrison, Mark RiedlList of authors in order
- Landing page
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https://uknowledge.uky.edu/cs_facpub/18Publisher landing page
- PDF URL
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https://uknowledge.uky.edu/cs_facpub/18Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://uknowledge.uky.edu/cs_facpub/18Direct OA link when available
- Concepts
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Natural (archaeology), Computer science, Natural language, Artificial intelligence, Natural language processing, History, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2023: 1, 2021: 1Per-year citation counts (last 5 years)
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