mlsanityData to 14 Oct 2026

Personalized Help for Optimizing Low-Skilled Users' Strategy

arXiv 2411.09109 ↗First posted 14 Nov 2024cs.CLcited by 3

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Abstract (arXiv)

AIs can beat humans in game environments; however, how helpful those agents are to human remains understudied. We augment CICERO, a natural language agent that demonstrates superhuman performance in Diplomacy, to generate both move and message advice based on player intentions. A dozen Diplomacy games with novice and experienced players, with varying advice settings, show that some of the generated advice is beneficial. It helps novices compete with experienced players and in some instances even surpass them. The mere presence of advice can be advantageous, even if players do not follow it.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 57 references that are in the map. First posted on arXiv 14 Nov 2024.

Builds on

22 of 57 references.

References · 22

  1. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  2. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 399
  3. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  4. 2018Manipulating and Measuring Model Interpretabilitycited by 256
  5. 2022CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilitiescited by 249
  6. 2018On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detectioncited by 159
  7. 2020Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systemscited by 139
  8. 2021HIVE: Evaluating the Human Interpretability of Visual Explanationscited by 105
  9. 2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanationscited by 83
  10. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  11. 2021Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Makingcited by 58
  12. 2023Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrongcited by 51
10 more
  1. 2021Modeling Strong and Human-Like Gameplay with KL-Regularized Searchcited by 50
  2. 2020Human-Level Performance in No-Press Diplomacy via Equilibrium Searchcited by 41
  3. 2019No Press Diplomacy: Modeling Multi-Agent Gameplaycited by 38
  4. 2021No-Press Diplomacy from Scratchcited by 31
  5. 2022Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Makingcited by 28
  6. 2021Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literaturecited by 27
  7. 2020Learning to Play No-Press Diplomacy with Best Response Policy Iterationcited by 26
  8. 2022Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Makingcited by 25
  9. 2024Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliancecited by 16
  10. 2024More Victories, Less Cooperation: Assessing Cicero's Diplomacy Playcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Userscited by 6
  2. 2025Should I Trust You? Detecting Deception in Negotiations using Counterfactual RLcited by 3
  3. 2026The Impact of AI Usage and Informativeness on Skill Development in Logical Reasoningcited by 1