MIT researchers use Battleship game to test and improve AI question-asking abilities
By
Alex Shipps | MIT CSAIL
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Summary
Researchers from MIT CSAIL and SEAS developed "Collaborative Battleship," a game where a captain asks natural language questions and a spotter responds to find hidden ships. They collected human gameplay data to build the BattleshipQA dataset, then tested state-of-the-art language models (like GPT-5) on the task. The AI models struggled to ask informative questions about hidden ships. However, a Monte Carlo inference strategy helped smaller agents carefully consider each inquiry and outperform larger systems at a fraction of the cost.
Key quotes
· 3 pulledThe researchers first had over 40 humans play the game together, collecting their questions and yes-no answers to build the 'BattleshipQA' dataset.
A Monte Carlo inference strategy helped small agents carefully consider each inquiry to outperform larger systems at a fraction of the cost.
AI models played 'Collaborative Battleship' together and struggled to ask informative questions about hidden ships.
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