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第60行： − − − [[Knowledge representation]]<ref name="Knowledge representation"/> and [[knowledge engineering]]<ref name="Knowledge engineering"/> are central to classical AI research. Some "expert systems" attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;<ref name="Representing categories and relations"/> situations, events, states and time;<ref name="Representing time"/> causes and effects;<ref name="Representing causation"/> knowledge about knowledge (what we know about what other people know) − 第69行：
第65行： − ;[[Default reasoning]] and the [[qualification problem]]: Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. [[John McCarthy (computer scientist)|John McCarthy]] identified this problem in 1969<ref name="Qualification problem"/> as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem.<ref name="Default reasoning and non-monotonic logic"/> − − Default reasoning and the qualification problem: Many of the things people know take the form of "working assumptions". For example, if a bird comes up in conversation, people typically picture an animal that is fist-sized, sings, and flies. None of these things are true about all birds. John McCarthy identified this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI research has explored a number of solutions to this problem. 第78行：
第71行： − − ;Subsymbolic form of some commonsense knowledge: Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed"{{sfn|Dreyfus|Dreyfus|1986}} or an art critic can take one look at a statue and realize that it is a fake.{{sfn|Gladwell|2005}} These are non-conscious and sub-symbolic intuitions or tendencies in the human brain.<ref name="Intuition"/> Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that [[situated artificial intelligence|situated AI]], [[computational intelligence]], or [[#Statistical|statistical AI]] will provide ways to represent this kind of knowledge.<ref name="Intuition"/> − − Subsymbolic form of some commonsense knowledge: Much of what people know is not represented as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed" or an art critic can take one look at a statue and realize that it is a fake. These are non-conscious and sub-symbolic intuitions or tendencies in the human brain. Knowledge like this informs, supports and provides a context for symbolic, conscious knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to represent this kind of knowledge. − +
→知识表示
=== 知识表示 ===
=== 知识表示 ===
[[File:GFO taxonomy tree.png|right|thumb|本体将知识表示为领域中的一组概念以及这些概念之间的关系。]]
[[File:GFO taxonomy tree.png|right|thumb|本体将知识表示为领域中的一组概念以及这些概念之间的关系。]]
传统的AI研究的重点是'''知识表示 Knowledge Representation'''<ref name="Knowledge representation">Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18</ref>和'''知识工程 Knowledge Engineering'''<ref name="Knowledge engineering">Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4</ref>。有些“专家系统”试图将某一小领域的专家所拥有的知识收集起来。此外，一些项目试图将普通人的“常识”收集到一个包含对世界的认知的知识的大数据库中。这些常识包括：对象、属性、类别和对象之间的关系；<ref name="Representing categories and relations">Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3</ref>情景、事件、状态和时间;<ref name="Representing time">Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2</ref>原因和结果;<ref name="Representing causation">Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337</ref>关于知识的知识(我们知道别人知道什么);和许多其他研究较少的领域。“存在的东西”的表示是本体，本体是被正式描述的对象、关系、概念和属性的集合，这样的形式可以让软件智能体能够理解它。本体的语义描述了逻辑概念、角色和个体，通常在Web本体语言中以类、属性和个体的形式实现。<ref>{{cite book |last=Sikos |first=Leslie F. |date=June 2017 |title=Description Logics in Multimedia Reasoning |url=https://www.springer.com/us/book/9783319540658 |location=Cham |publisher=Springer |isbn=978-3-319-54066-5 |doi=10.1007/978-3-319-54066-5 |url-status=live |archiveurl=https://web.archive.org/web/20170829120912/https://www.springer.com/us/book/9783319540658 |archivedate=29 August 2017 |df=dmy-all }}</ref>最常见的本体称为'''<font color=#ff8000>上本体 Upper Ontology</font>''',它试图为所有其他知识提供一个基础，<ref name="Ontology"/>它充当涵盖有关特定知识领域(兴趣领域或关注领域)的特定知识的领域本体之间的中介。这种形式化的知识表示可以用于基于内容的索引和检索，<ref>{{cite journal|last1=Smoliar|first1=Stephen W.|last2=Zhang|first2=HongJiang|title=Content based video indexing and retrieval|journal=IEEE Multimedia|date=1994|volume=1|issue=2|pages=62–72|doi=10.1109/93.311653}}</ref>场景解释，<ref>{{cite journal|last1=Neumann|first1=Bernd|last2=Möller|first2=Ralf|title=On scene interpretation with description logics|journal=Image and Vision Computing|date=January 2008|volume=26|issue=1|pages=82–101|doi=10.1016/j.imavis.2007.08.013}}</ref>临床决策，<ref>{{cite journal|last1=Kuperman|first1=G. J.|last2=Reichley|first2=R. M.|last3=Bailey|first3=T. C.|title=Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations|journal=Journal of the American Medical Informatics Association|date=1 July 2006|volume=13|issue=4|pages=369–371|doi=10.1197/jamia.M2055|pmid=16622160|pmc=1513681}}</ref>知识发现(从大型数据库中挖掘“有趣的”和可操作的推论)<ref>{{cite journal|last1=MCGARRY|first1=KEN|title=A survey of interestingness measures for knowledge discovery|journal=The Knowledge Engineering Review|date=1 December 2005|volume=20|issue=1|page=39|doi=10.1017/S0269888905000408|url=https://semanticscholar.org/paper/baf7f99e1b567868a6dc6238cc5906881242da01}}</ref>等领域。<ref>{{cite conference |url= |title=Automatic annotation and semantic retrieval of video sequences using multimedia ontologies |last1=Bertini |first1=M |last2=Del Bimbo |first2=A |last3=Torniai |first3=C |date=2006 |publisher=ACM |book-title=MM '06 Proceedings of the 14th ACM international conference on Multimedia |pages=679–682 |location=Santa Barbara |conference=14th ACM international conference on Multimedia}}</ref>
传统的AI研究的重点是'''知识表示 Knowledge Representation'''<ref name="Knowledge representation">Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18</ref>和'''知识工程 Knowledge Engineering'''<ref name="Knowledge engineering">Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4</ref>。有些“专家系统”试图将某一小领域的专家所拥有的知识收集起来。此外，一些项目试图将普通人的“常识”收集到一个包含对世界的认知的知识的大数据库中。这些常识包括：对象、属性、类别和对象之间的关系；<ref name="Representing categories and relations">Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3</ref>情景、事件、状态和时间;<ref name="Representing time">Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2</ref>原因和结果;<ref name="Representing causation">Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337</ref>关于知识的知识(我们知道别人知道什么);和许多其他研究较少的领域。“存在的东西”的表示是本体，本体是被正式描述的对象、关系、概念和属性的集合，这样的形式可以让软件智能体能够理解它。本体的语义描述了逻辑概念、角色和个体，通常在Web本体语言中以类、属性和个体的形式实现。<ref>{{cite book |last=Sikos |first=Leslie F. |date=June 2017 |title=Description Logics in Multimedia Reasoning |url=https://www.springer.com/us/book/9783319540658 |location=Cham |publisher=Springer |isbn=978-3-319-54066-5 |doi=10.1007/978-3-319-54066-5 |url-status=live |archiveurl=https://web.archive.org/web/20170829120912/https://www.springer.com/us/book/9783319540658 |archivedate=29 August 2017 |df=dmy-all }}</ref>最常见的本体称为'''<font color=#ff8000>上本体 Upper Ontology</font>''',它试图为所有其他知识提供一个基础，<ref name="Ontology"/>它充当涵盖有关特定知识领域(兴趣领域或关注领域)的特定知识的领域本体之间的中介。这种形式化的知识表示可以用于基于内容的索引和检索，<ref>{{cite journal|last1=Smoliar|first1=Stephen W.|last2=Zhang|first2=HongJiang|title=Content based video indexing and retrieval|journal=IEEE Multimedia|date=1994|volume=1|issue=2|pages=62–72|doi=10.1109/93.311653}}</ref>场景解释，<ref>{{cite journal|last1=Neumann|first1=Bernd|last2=Möller|first2=Ralf|title=On scene interpretation with description logics|journal=Image and Vision Computing|date=January 2008|volume=26|issue=1|pages=82–101|doi=10.1016/j.imavis.2007.08.013}}</ref>临床决策，<ref>{{cite journal|last1=Kuperman|first1=G. J.|last2=Reichley|first2=R. M.|last3=Bailey|first3=T. C.|title=Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations|journal=Journal of the American Medical Informatics Association|date=1 July 2006|volume=13|issue=4|pages=369–371|doi=10.1197/jamia.M2055|pmid=16622160|pmc=1513681}}</ref>知识发现(从大型数据库中挖掘“有趣的”和可操作的推论)<ref>{{cite journal|last1=MCGARRY|first1=KEN|title=A survey of interestingness measures for knowledge discovery|journal=The Knowledge Engineering Review|date=1 December 2005|volume=20|issue=1|page=39|doi=10.1017/S0269888905000408|url=https://semanticscholar.org/paper/baf7f99e1b567868a6dc6238cc5906881242da01}}</ref>等领域。<ref>{{cite conference |url= |title=Automatic annotation and semantic retrieval of video sequences using multimedia ontologies |last1=Bertini |first1=M |last2=Del Bimbo |first2=A |last3=Torniai |first3=C |date=2006 |publisher=ACM |book-title=MM '06 Proceedings of the 14th ACM international conference on Multimedia |pages=679–682 |location=Santa Barbara |conference=14th ACM international conference on Multimedia}}</ref>
知识表示中最困难的问题是:
知识表示中最困难的问题是:
'''默认推理和资格问题''' : 人们对事物的认知常常基于一个可行的假设。提到鸟，人们通常会想象一只拳头大小、会唱歌、会飞的动物，但并不是所有鸟类都有这样的特性。1969年John McCarthy<ref>McCarthy, John; Hayes, P. J. (1969). "Some philosophical problems from the standpoint of artificial intelligence". Machine Intelligence. 4: 463–502. CiteSeerX 10.1.1.85.5082.</ref><ref>Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2.</ref>将其归咎于限定性问题:对于AI研究人员所关心的任何常识性规则来说，大量的例外往往存在。几乎没有什么在抽象逻辑角度是完全真或完全假。AI研究探索了许多解决这个问题的方法。<ref>Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 978-0-19-510270-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020.</ref><ref>Luger, George; Stubblefield, William (2004). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). Benjamin/Cummings. ISBN 978-0-8053-4780-7.</ref><ref>Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the original on 26 July 2020. Retrieved 18 November 2019.</ref>
'''默认推理和资格问题''' : 人们对事物的认知常常基于一个可行的假设。提到鸟，人们通常会想象一只拳头大小、会唱歌、会飞的动物，但并不是所有鸟类都有这样的特性。1969年John McCarthy<ref>McCarthy, John; Hayes, P. J. (1969). "Some philosophical problems from the standpoint of artificial intelligence". Machine Intelligence. 4: 463–502. CiteSeerX 10.1.1.85.5082.</ref><ref>Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2.</ref>将其归咎于限定性问题:对于AI研究人员所关心的任何常识性规则来说，大量的例外往往存在。几乎没有什么在抽象逻辑角度是完全真或完全假。AI研究探索了许多解决这个问题的方法。<ref>Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 978-0-19-510270-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020.</ref><ref>Luger, George; Stubblefield, William (2004). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). Benjamin/Cummings. ISBN 978-0-8053-4780-7.</ref><ref>Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. Archived from the original on 26 July 2020. Retrieved 18 November 2019.</ref>
'''常识的广度''': 常人掌握的“元常识”的数量是非常大的。试图建立一个完整的常识知识库（例如Cyc）的研究项目需要大量费力的本体工程——它们必须一次手工构建一个复杂的概念。<ref>Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2</ref><ref>Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0-465-02997-3</ref><ref>Moravec, Hans (1988). Mind Children. Harvard University Press. ISBN 978-0-674-57616-2.</ref><ref>Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. </ref>
'''常识的广度''': 常人掌握的“元常识”的数量是非常大的。试图建立一个完整的常识知识库（例如Cyc）的研究项目需要大量费力的本体工程——它们必须一次手工构建一个复杂的概念。<ref>Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2</ref><ref>Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0-465-02997-3</ref><ref>Moravec, Hans (1988). Mind Children. Harvard University Press. ISBN 978-0-674-57616-2.</ref><ref>Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4. </ref>
'''一些常识性知识的子符号形式''': 人们所知道的许多东西必不能用可以口头表达的“事实”或“陈述”描述。例如，一个国际象棋大师会避免下某个位置，因为觉得这步棋“感觉上太激进”；<ref>Dreyfus, Hubert; Dreyfus, Stuart (1986). Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. Oxford, UK: Blackwell. ISBN 978-0-02-908060-3.</ref>或者一个艺术评论家可以看一眼雕像，就知道它是假的。<ref>Gladwell, Malcolm (2005). Blink. New York: Little, Brown and Co. ISBN 978-0-316-17232-5.</ref>这些是人类大脑中无意识和亚符号的直觉。这种知识为符号化的、有意识的知识提供信息和语境。<ref>Dreyfus, Hubert; Dreyfus, Stuart (1986). Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. Oxford, UK: Blackwell. ISBN 978-0-02-908060-3.</ref><ref>Hawkins, Jeff; Blakeslee, Sandra (2005). On Intelligence. New York, NY: Owl Books. ISBN 978-0-8050-7853-4.</ref>与子符号推理的相关问题一样，我们希望情境AI、计算智能或统计AI能够表示这类知识。
'''一些常识性知识的子符号形式''': 人们所知道的许多东西必不能用可以口头表达的“事实”或“陈述”描述。例如，一个国际象棋大师会避免下某个位置，因为觉得这步棋“感觉上太激进”；<ref>Dreyfus, Hubert; Dreyfus, Stuart (1986). Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. Oxford, UK: Blackwell. ISBN 978-0-02-908060-3.</ref>或者一个艺术评论家可以看一眼雕像，就知道它是假的。<ref>Gladwell, Malcolm (2005). Blink. New York: Little, Brown and Co. ISBN 978-0-316-17232-5.</ref>这些是人类大脑中无意识和亚符号的直觉。这种知识为符号化的、有意识的知识提供信息和语境。<ref>Dreyfus, Hubert; Dreyfus, Stuart (1986). Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. Oxford, UK: Blackwell. ISBN 978-0-02-908060-3.</ref><ref>Hawkins, Jeff; Blakeslee, Sandra (2005). On Intelligence. New York, NY: Owl Books. ISBN 978-0-8050-7853-4.</ref>与子符号推理的相关问题一样，我们希望情境AI、计算智能或统计AI能够表示这类知识。
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=== 规划 ===
=== 规划 ===