用于优化说服性对话策略的因果发现与反事实推理
Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies
AI总结：
针对现有说服性对话系统难适配动态用户状态的问题，提出结合因果发现、反事实推理与强化学习的方法，在PersuasionForGood数据集上实现了说服效果的提升。
摘要：
为用户定制说服性对话能提升说服效果。然而，现有对话系统往往难以适应动态变化的用户状态。本文提出一种新方法，利用因果发现和反事实推理来优化系统的说服能力与效果。我们采用最稀疏排列贪心松弛（Greedy Relaxation of the Sparsest Permutation, GRaSP）算法识别用户与系统话语策略之间的因果关系，将用户策略视为状态，系统策略视为动作。GRaSP将用户策略识别为影响系统响应的因果因素，为双向条件生成对抗网络（Bidirectional Conditional Generative Adversarial Networks, BiCoGAN）提供依据，生成系统的反事实话语。随后，我们使用决斗双深度Q网络（Dueling Double Deep Q-Network, D3QN）模型利用反事实数据确定选择系统话语的最优策略。我们在PersuasionForGood数据集上的实验表明，与基线方法相比，本文方法的说服效果有可衡量的提升。累积奖励和Q值的观测增长凸显了因果发现在增强反事实推理、优化在线对话系统强化学习策略方面的有效性。
英文摘要：
Tailoring persuasive conversations to users leads to more effective persuasion. However, existing dialogue systems often struggle to adapt to dynamically evolving user states. This paper presents a novel method that leverages causal discovery and counterfactual reasoning for optimizing system persuasion capability and outcomes. We employ the Greedy Relaxation of the Sparsest Permutation (GRaSP) algorithm to identify causal relationships between user and system utterance strategies, treating user strategies as states and system strategies as actions. GRaSP identifies user strategies as causal factors influencing system responses, which inform Bidirectional Conditional Generative Adversarial Networks (BiCoGAN) in generating counterfactual utterances for the system. Subsequently, we use the Dueling Double Deep Q-Network (D3QN) model to utilize counterfactual data to determine the best policy for selecting system utterances. Our experiments with the PersuasionForGood dataset show measurable improvements in persuasion outcomes using our approach over baseline methods. The observed increase in cumulative rewards and Q-values highlights the effectiveness of causal discovery in enhancing counterfactual reasoning and optimizing reinforcement learning policies for online dialogue systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
