arXiv cs/03070702003-07-30cs.AIcs.LO

动态系统中的信念建模，第一部分：基础

Modeling Belief in Dynamic Systems, Part I: Foundations

Nir Friedman, Joseph Y. Halpern

发表信息：: Aritificial Intelligence 95:2, 1997, pp. 257-316

摘要：

信念改变是人工智能中的一个基本问题：智能体必须不断更新自己的信念以适应新的观察结果。近年来，有很多工作的公理化特征的信念变化。我们认为，一个更好的理解信念的变化，可以通过检查适当的语义模型。在本文中，我们提出了一个一般的框架，在其中模型的信念变化。我们首先根据知识和可信度来定义信念：如果一个主体知道p比它的否定更可信，他就相信p。然后，我们考虑一些属性定义的知识和可验证性之间的相互作用，并显示这些属性如何影响信仰的属性。特别是，我们表明，通过假设两个最自然的属性，信念成为一个KD 45运营商。最后，我们把时间添加到图片中。这给了我们一个框架，在这个框架中，我们可以讨论知识、可解释性（以及信念）和时间，这扩展了Halpern和Fagin在多智能体系统中建模知识的框架。然后，我们研究“最小变化”的问题。这个概念可以通过使用先验概率来捕捉，先验概率是先验概率的一种类似物，可以通过“条件”来更新。我们通过例子表明，条件的可扩展性措施可以捕捉到许多感兴趣的情况。在一个配套文件中，我们展示了两个最好的研究方案的信念变化，信念revisionand信念更新，适合我们的框架。

英文摘要：

Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations of belief change. We claim that a better understanding of belief change can be gained from examining appropriate semantic models. In this paper we propose a general framework in which to model belief change. We begin by defining belief in terms of knowledge and plausibility: an agent believes p if he knows that p is more plausible than its negation. We then consider some properties defining the interaction between knowledge and plausibility, and show how these properties affect the properties of belief. In particular, we show that by assuming two of the most natural properties, belief becomes a KD45 operator. Finally, we add time to the picture. This gives us a framework in which we can talk about knowledge, plausibility (and hence belief), and time, which extends the framework of Halpern and Fagin for modeling knowledge in multi-agent systems. We then examine the problem of ``minimal change''. This notion can be captured by using prior plausibilities, an analogue to prior probabilities, which can be updated by ``conditioning''. We show by example that conditioning on a plausibility measure can capture many scenarios of interest. In a companion paper, we show how the two best-studied scenarios of belief change, belief revisionand belief update, fit into our framework.

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。

摘要：

英文摘要：

相关论文