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MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks

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arxiv 2109.06275 v1 pith:XEXKZVJG submitted 2021-09-13 cs.AI cs.CLcs.CVcs.LG

classification cs.AIcs.CLcs.CVcs.LG
keywords humancollaborativemindtheorysituatedtasksworldable
verification ladder T0 review T1 audit T2 compute T3 formal
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An ideal integration of autonomous agents in a human world implies that they are able to collaborate on human terms. In particular, theory of mind plays an important role in maintaining common ground during human collaboration and communication. To enable theory of mind modeling in situated interactions, we introduce a fine-grained dataset of collaborative tasks performed by pairs of human subjects in the 3D virtual blocks world of Minecraft. It provides information that captures partners' beliefs of the world and of each other as an interaction unfolds, bringing abundant opportunities to study human collaborative behaviors in situated language communication. As a first step towards our goal of developing embodied AI agents able to infer belief states of collaborative partners in situ, we build and present results on computational models for several theory of mind tasks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Morae: Proactively Pausing UI Agents for User Choices

    cs.HC 2025-08 conditional novelty 6.0 of 10

    Morae, a UI agent that proactively pauses at ambiguous decision points, helps blind and low-vision users complete more tasks and express preferences better than fully autonomous agents.

  2. Referential ambiguity and clarification requests: comparing human and LLM behaviour

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Humans seldom ask clarification questions for referential ambiguity, while LLMs ask them more often, and reasoning prompts increase LLM question frequency and relevance.

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