A Qualitative Model for Reasoning about Path and Support
A hybrid qualitative reasoning model with center-of-mass stability logic enables explainable path-planning and stability guidance for the Camelot Jr. block-puzzle game.
The paper presents a hybrid qualitative reasoning (QR) model for Camelot Jr., a block-puzzle game requiring multi-level bridge construction between two avatars on separate towers. It integrates mathematical center-of-mass stability logic into a symbolic qualitative solver to handle the game's precise physics. Because QR models reason over symbolic representations, the agent translates game states into interpretable feedback, supporting human-like tutoring, player guidance, and spatial skill training for children.
- Integrates mathematical center-of-mass stability logic into a qualitative solver for precise game physics.
- Symbolic reasoning enables interpretable game-state feedback for human-like tutoring and player guidance.
- Targets spatial skill training in children through explainable game-playing agents.
From the original article
1 min read at arxiv.orgSpatial reasoning abilities correlate strongly with performance in STEM fields. Games offer a compelling medium for training these critical skills in developing children who have a natural proclivity for play. However, to facilitate human-like tutoring and player guidance, these games require an AI agent capable of making commonsense inferences from spatial events.
By Abhishek Jaiswal · arxiv.org
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