Representing and reasoning with probabilistic knowledge - a logical approach to probabilities
Representing and reasoning with probabilistic knowledge - a logical approach to probabilities
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DOI：
10.2307/1423204
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发表时间：
1991-01
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影响因子：
--
通讯作者：
F. Bacchus
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文献类型：
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作者：
F. Bacchus
概率信息在智能系统中有许多用途。这本书探索了用概率信息表示和推理的逻辑形式，这些信息将对非单调推理、概率应用和知识表示的研究人员特别有价值。它表明概率并不局限于特定的应用，如专家系统；它们在智能系统的形式设计和一般规范中扮演着重要的角色。法海姆·巴克斯专注于两个不同的概率概念：一个命题，涉及信任度，另一个比例，涉及统计学。他为每种概率类型构造了具有不同语义的不同逻辑，这是可用于用概率表示和推理的形式工具的重大进步。这些逻辑可以表示各种各样的定性断言，消除了对精确点值概率的要求，并且它们可以表示一阶逻辑信息。这些逻辑也有证明理论，它给出了一类推理的形式规范，该推理包含并集成了迄今为止在人工智能中发展的大多数概率推理方案。使用新的逻辑工具将统计和命题概率联系起来，巴克斯还提出了一个直接推理系统，其中信任程度可以从统计知识中推断出来，并演示了如何应用该机制来产生一个强大的、直观地令人满意的可废止或缺省推理系统。命题概率。统计概率。将统计概率和命题概率结合起来，可以从统计知识中进行默认推论。
Probabilistic information has many uses in an intelligent system. This book explores logical formalisms for representing and reasoning with probabilistic information that will be of particular value to researchers in nonmonotonic reasoning, applications of probabilities, and knowledge representation. It demonstrates that probabilities are not limited to particular applications, like expert systems; they have an important role to play in the formal design and specification of intelligent systems in general.Fahiem Bacchus focuses on two distinct notions of probabilities: one propositional, involving degrees of belief, the other proportional, involving statistics. He constructs distinct logics with different semantics for each type of probability that are a significant advance in the formal tools available for representing and reasoning with probabilities. These logics can represent an extensive variety of qualitative assertions, eliminating requirements for exact point-valued probabilities, and they can represent first-order logical information. The logics also have proof theories which give a formal specification for a class of reasoning that subsumes and integrates most of the probabilistic reasoning schemes so far developed in AI.Using the new logical tools to connect statistical with propositional probability, Bacchus also proposes a system of direct inference in which degrees of belief can be inferred from statistical knowledge and demonstrates how this mechanism can be applied to yield a powerful and intuitively satisfying system of defeasible or default reasoning.Contents: Introduction. Propositional Probabilities. Statistical Probabilities. Combining Statistical and Propositional Probabilities Default Inferences from Statistical Knowledge.