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Using Reinforcement Learning to Manage Communications Between Humans, Artificial Agents in an Evacuation Scenario

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cs.CL 1

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2026 1

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UNVERDICTED 1

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DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents

cs.CL · 2026-07-02 · unverdicted · novelty 5.0

DiPS uses a trained critic to select persuasion policies via Q-learning in a fire-rescue evacuation task and reports higher success rates than zero-shot LLM or RAG baselines in both simulation and human trials.

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  • DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents cs.CL · 2026-07-02 · unverdicted · none · ref 24

    DiPS uses a trained critic to select persuasion policies via Q-learning in a fire-rescue evacuation task and reports higher success rates than zero-shot LLM or RAG baselines in both simulation and human trials.