Papers by Wichayaporn Wongkamjan

    3 papers
    Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL (2025.findings-acl)

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    Challenge: Future human-AI interaction tools can build on our methods for deception detection by triggering friction to give users a chance to interrogate suspicious proposals.
    Approach: They propose to use CTRL-D to detect deception in a board game called Diplomacy . CTRL is a counterfactual RL that has a good recall and almost perfect precision . future tools could build on this to reevaluate trust in suspicious negotiations .
    Outcome: The proposed method detects human deception with a high precision when compared to a Large Language Model approach that flags many true messages as deceptive.
    Personalized Help for Optimizing Low-Skilled Users’ Strategy (2025.naacl-short)

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    Challenge: a natural language agent generates moves and messages based on player intentions . a dozen games with novice and experienced players generate useful advice .
    Approach: a team of researchers augment a natural language agent to generate move and message advice . they use a game to simulate the intentions of novice and experienced players .
    Outcome: The enhanced agent generates move and message advice based on player intentions . the agent helps novices compete with experienced players and even surpass them .
    More Victories, Less Cooperation: Assessing Cicero’s Diplomacy Play (2024.acl-long)

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    Challenge: Diplomacy is a boardgame that offers a challenge for communicative and cooperative AI.
    Approach: They run two dozen games with Cicero and annotate in-game communication with abstract meaning representation to separate in- game tactics from general language.
    Outcome: The proposed method can outperform Cicero in communicating with humans, but it's difficult to deceive and persuade AI.

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