In dynamic epistemic logic (Van Ditmarsch, Van Der Hoek, & Kooi, 2008) it is customary to use an action frame (Baltag & Moss, 2004; Baltag, Moss, & Solecki, 1998) to describe different views of a single action. In this article, action frames are extended to add or remove agents, we call these agent-update frames. This can be done selectively so that only some specified agents get information of the update, which can be used to model several interesting examples such as private update and deception, studied earlier by Baltag and Moss (2004); Sakama (2015); Van Ditmarsch, Van Eijck, Sietsma, and Wang (2012). The product update of a Kripke model by an action frame is an abbreviated way of describing the transformed Kripke model which is the result of performing the action. This is substantially extended to a sum-product update of a Kripke model by an agent-update frame in the new setting. These ideas are applied to an AI problem of modelling a story. We show that dynamic epistemic logics, with update modalities now based on agent-update frames, continue to have sound and complete proof systems. Decision procedures for model checking and satisfiability have expected complexity. For a sublanguage, there are polynomial space algorithms.
翻译:在动态认知逻辑(Van Ditmarsch,Van Der Hoek和Kooi,2008)中,通常使用动作框架(Baltag&Moss,2004; Baltag,Moss和Solecki,1998)描述单个动作的不同视图。在本文中,将动作框架扩展到添加或删除代理人,我们称之为代理人更新框架。可以有选择地执行此操作,以便仅向一些指定的代理人提供更新信息,这可用于建模几个有趣的示例,例如Baltag和Moss(2004); Sakama(2015); Van Ditmarsch,Van Eijck,Sietsma和Wang(2012)早期研究的私人更新和欺骗。用动作框架将Kripke模型的产品更新简化为描述执行该动作的转换Kripke模型的缩写。在新设置中,将Kripke模型的和积更新扩展为代理人更新框架的和积更新。这些思想应用于模拟故事的AI问题。我们表明,具有基于代理人更新框架的更新模态的动态认知逻辑仍具有完整的证明系统和正确性。模型检查和可满足性的决策过程具有预期的复杂性。对于一个子语言,存在多项式空间算法。