Goal-hiding information-seeking dialogues: A formal framework

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Andreas Brännström, Virginia Dignum, Juan Carlos Nieves
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引用次数: 0

Abstract

We consider a type of information-seeking dialogue between a seeker agent and a respondent agent, where the seeker estimates the respondent to not be willing to share a particular set of sought-after information. Hence, the seeker postpones (hides) its goal topic, related to the respondent's sensitive information, until the respondent is perceived as willing to talk about it. In the intermediate process, the seeker opens other topics to steer the dialogue tactfully towards the goal. Such dialogue strategies, which we refer to as goal-hiding strategies, are common in diverse contexts such as criminal interrogations and medical assessments, involving sensitive topics. Conversely, in malicious online interactions like social media extortion, similar strategies might aim to manipulate individuals into revealing information or agreeing to unfavorable terms. This paper proposes a formal dialogue framework for understanding goal-hiding strategies. The dialogue framework uses Quantitative Bipolar Argumentation Frameworks (QBAFs) to assign willingness scores to topics. An initial willingness for each topic is modified by considering how topics promote (support) or demote (attack) other topics. We introduce a method to identify relations among topics by considering a respondent's shared information. Finally, we introduce a gradual semantics to estimate changes in willingness as new topics are opened. Our formal analysis and empirical evaluation show the system's compliance with privacy-preserving safety properties. A formal understanding of goal-hiding strategies opens up a range of practical applications; For instance, a seeker agent may plan with goal-hiding to enhance privacy in human-agent interactions. Similarly, an observer agent (third-party) may be designed to enhance social media security by detecting goal-hiding strategies employed by users' interlocutors.
目标隐藏的信息搜索对话:正式框架
我们考虑了寻求者代理和应答者代理之间的一种信息寻求对话,在这种对话中,寻求者估计应答者不愿意分享所寻求的一组特定信息。因此,寻求者会推迟(隐藏)与应答者敏感信息相关的目标话题,直到应答者被认为愿意谈论该话题为止。在中间过程中,寻求者开启其他话题,巧妙地将对话引向目标。这种对话策略,我们称之为目标隐藏策略,常见于刑事审讯和医疗评估等各种涉及敏感话题的场合。相反,在社交媒体勒索等恶意在线互动中,类似的策略可能旨在操纵个人透露信息或同意不利条件。本文提出了一个用于理解目标隐藏策略的正式对话框架。该对话框架使用定量双极论证框架(QBAF)为话题分配意愿分数。通过考虑话题如何促进(支持)或贬低(攻击)其他话题,对每个话题的初始意愿进行修改。我们引入了一种方法,通过考虑受访者的共享信息来识别话题之间的关系。最后,我们引入了渐进语义来估计新话题开启时意愿的变化。我们的形式分析和经验评估表明,该系统符合隐私保护的安全属性。对目标隐藏策略的正式理解开辟了一系列实际应用;例如,寻求者代理可以通过目标隐藏计划来增强人与代理交互中的隐私保护。同样,观察者代理(第三方)也可以通过检测用户对话者采用的目标隐藏策略来增强社交媒体的安全性。
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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