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[[Logic]]<ref name="Logic"/> is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the [[satplan]] algorithm uses logic for [[automated planning and scheduling|planning]]<ref name="Satplan"/> and [[inductive logic programming]] is a method for [[machine learning|learning]].<ref name="Symbolic learning techniques"/>

[[Logic]]<ref name="Logic"/> is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the [[satplan]] algorithm uses logic for [[automated planning and scheduling|planning]]<ref name="Satplan"/> and [[inductive logic programming]] is a method for [[machine learning|learning]].<ref name="Symbolic learning techniques"/>

逻辑<ref name="Logic"/>被用来表示知识和解决问题，还可以应用到其他问题上。例如，satplan 算法就使用逻辑进行规划<ref name="Satplan"/>。另外，归纳逻辑编程是一种学习方法。

逻辑<ref name="Russell & Norvig 2003"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/><ref>"ACM Computing Classification System: Artificial intelligence". ACM. 1998. ~I.2.3 and ~I.2.4. </ref>被用来表示知识和解决问题，还可以应用到其他问题上。例如，satplan 算法就使用逻辑进行规划<ref name="Satplan"/>。另外，归纳逻辑编程是一种学习方法。

Several different forms of logic are used in AI research. [[Propositional logic]]<ref name="Propositional logic"/> involves [[truth function]]s such as "or" and "not". [[First-order logic]]<ref name="First-order logic"/> adds [[quantifier (logic)|quantifiers]] and [[predicate (mathematical logic)|predicates]], and can express facts about objects, their properties, and their relations with each other. [[Fuzzy set theory]] assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. [[Fuzzy logic]] is successfully used in [[control system]]s to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.{{efn|"There exist many different types of uncertainty, vagueness, and ignorance... [We] independently confirm the inadequacy of systems for reasoning about uncertainty that propagates numerical factors according to only to which connectives appear in assertions."<ref>{{cite journal|last1=Elkan|first1=Charles|title=The paradoxical success of fuzzy logic|journal=IEEE Expert|date=1994|volume=9|issue=4|pages=3–49|doi=10.1109/64.336150|citeseerx=10.1.1.100.8402}}</ref>}}<ref name="Fuzzy logic"/><ref>{{cite news|title=What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?|url=https://www.scientificamerican.com/article/what-is-fuzzy-logic-are-t/|accessdate=5 May 2018|work=Scientific American|language=en}}</ref>

Several different forms of logic are used in AI research. [[Propositional logic]]<ref name="Propositional logic"/> involves [[truth function]]s such as "or" and "not". [[First-order logic]]<ref name="First-order logic"/> adds [[quantifier (logic)|quantifiers]] and [[predicate (mathematical logic)|predicates]], and can express facts about objects, their properties, and their relations with each other. [[Fuzzy set theory]] assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. [[Fuzzy logic]] is successfully used in [[control system]]s to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.{{efn|"There exist many different types of uncertainty, vagueness, and ignorance... [We] independently confirm the inadequacy of systems for reasoning about uncertainty that propagates numerical factors according to only to which connectives appear in assertions."<ref>{{cite journal|last1=Elkan|first1=Charles|title=The paradoxical success of fuzzy logic|journal=IEEE Expert|date=1994|volume=9|issue=4|pages=3–49|doi=10.1109/64.336150|citeseerx=10.1.1.100.8402}}</ref>}}<ref name="Fuzzy logic"/><ref>{{cite news|title=What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?|url=https://www.scientificamerican.com/article/what-is-fuzzy-logic-are-t/|accessdate=5 May 2018|work=Scientific American|language=en}}</ref>

'''缺省逻辑 Default Logics'''、'''非单调逻辑 Non-monotonic Logics'''、'''限制逻辑 Circumscription'''和'''模态逻辑 Modal Logics'''<ref name="Default reasoning and non-monotonic logic"/>，都用逻辑形式来解决缺省推理和限定问题。一些逻辑扩展被用于处理特定的知识领域，例如:'''描述逻辑 Description Logics'''<ref name="Representing categories and relations"/> 、情景演算、事件演算、'''流态演算 Fluent Calculus'''（用于表示事件和时间）<ref name="Representing time"/>、因果演算<ref name="Representing causation"/>、信念演算(信念修正)<ref>"The Belief Calculus and Uncertain Reasoning", Yen-Teh Hsia</ref>、和模态逻辑<ref name="Representing knowledge about knowledge"/>。人们也设计了对多主体系统中出现的矛盾或不一致陈述进行建模的逻辑，如次协调逻辑。

'''缺省逻辑 Default Logics'''、'''非单调逻辑 Non-monotonic Logics'''、'''限制逻辑 Circumscription'''和'''模态逻辑 Modal Logics'''<ref name="Default reasoning and non-monotonic logic"/>，都用逻辑形式来解决缺省推理和限定问题。一些逻辑扩展被用于处理特定的知识领域，例如:'''描述逻辑 Description Logics'''<ref name="Representing categories and relations"/> 、情景演算、事件演算、'''流态演算 Fluent Calculus'''（用于表示事件和时间）<ref name="Representing time"/>、因果演算<ref name="Representing causation"/>、信念演算(信念修正)<ref>"The Belief Calculus and Uncertain Reasoning", Yen-Teh Hsia</ref>、和模态逻辑<ref name="Representing knowledge about knowledge"/>。人们也设计了对多主体系统中出现的矛盾或不一致陈述进行建模的逻辑，如次协调逻辑。

===不确定推理的概率方法===

===不确定推理的概率方法===