MindCraft: Theory of Mind Modeling for Situated Dialogue in
Collaborative Tasks
- URL: http://arxiv.org/abs/2109.06275v1
- Date: Mon, 13 Sep 2021 19:26:19 GMT
- Title: MindCraft: Theory of Mind Modeling for Situated Dialogue in
Collaborative Tasks
- Authors: Cristian-Paul Bara, Sky CH-Wang, Joyce Chai
- Abstract summary: Theory of mind plays an important role in maintaining common ground during human collaboration and communication.
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.
- Score: 2.5725755841426623
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task [56.92961847155029]
Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others.
Mutual Theory of Mind (MToM) arises when AI agents with ToM capability collaborate with humans.
We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent.
arXiv Detail & Related papers (2024-09-13T13:19:48Z) - Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World [13.005764902339523]
We design a blocks-world environment where two agents, each having unique goals and skills, build a target structure together.
To complete the goals, they can act in the world and communicate in natural language.
We adopt chain-of-thought prompts that include intermediate reasoning steps to model the partner's state and identify and correct execution errors.
arXiv Detail & Related papers (2024-03-30T04:48:38Z) - Building Cooperative Embodied Agents Modularly with Large Language
Models [104.57849816689559]
We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments.
We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework.
Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
arXiv Detail & Related papers (2023-07-05T17:59:27Z) - MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation [62.44907105496227]
MindDial is a novel conversational framework that can generate situated free-form responses with theory-of-mind modeling.
We introduce an explicit mind module that can track the speaker's belief and the speaker's prediction of the listener's belief.
Our framework is applied to both prompting and fine-tuning-based models, and is evaluated across scenarios involving both common ground alignment and negotiation.
arXiv Detail & Related papers (2023-06-27T07:24:32Z) - Towards Collaborative Plan Acquisition through Theory of Mind Modeling
in Situated Dialogue [10.233928711341825]
Collaborative tasks often begin with partial task knowledge and incomplete initial plans from each partner.
This paper takes a step towards collaborative plan acquisition, where humans and agents strive to learn and communicate with each other.
We formulate a novel problem for agents to predict the missing task knowledge for themselves and for their partners based on rich perceptual and dialogue history.
arXiv Detail & Related papers (2023-05-18T19:42:04Z) - Human-guided Collaborative Problem Solving: A Natural Language based
Framework [74.27063862727849]
Our framework consists of three components -- a natural language engine that parses the language utterances to a formal representation and vice-versa.
We illustrate the ability of this framework to address the key challenges of collaborative problem solving by demonstrating it on a collaborative building task in a Minecraft-based blocksworld domain.
arXiv Detail & Related papers (2022-07-19T21:52:37Z) - BOSS: A Benchmark for Human Belief Prediction in Object-context
Scenarios [14.23697277904244]
This paper uses the combined knowledge of Theory of Mind (ToM) and Object-Context Relations to investigate methods for enhancing collaboration between humans and autonomous systems.
We propose a novel and challenging multimodal video dataset for assessing the capability of artificial intelligence (AI) systems in predicting human belief states in an object-context scenario.
arXiv Detail & Related papers (2022-06-21T18:29:17Z) - Few-shot Language Coordination by Modeling Theory of Mind [95.54446989205117]
We study the task of few-shot $textitlanguage coordination$.
We require the lead agent to coordinate with a $textitpopulation$ of agents with different linguistic abilities.
This requires the ability to model the partner's beliefs, a vital component of human communication.
arXiv Detail & Related papers (2021-07-12T19:26:11Z) - Joint Mind Modeling for Explanation Generation in Complex Human-Robot
Collaborative Tasks [83.37025218216888]
We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations.
The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications.
Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
arXiv Detail & Related papers (2020-07-24T23:35:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.