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1956年AI作为一门学科被建立起来，后来经历过几段乐观时期<ref name="Optimism of early AI">Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109.</ref><ref name="AI in the 80s">Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248</ref>与紧随而来的亏损以及缺乏资金的困境（也就是“AI寒冬”<ref name="First AI winter">First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201</ref><ref name="Second AI winter">Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318</ref>），每次又找到了新的出路，取得了新的成果和新的投资<ref name="AI in the 80s"/><ref name="AI in 2000s">AI becomes hugely successful in the early 21st century * Clark 2015b</ref>。AI 研究在其一生中尝试并放弃了许多不同的方法，包括模拟大脑、模拟人类问题解决、形式逻辑、大型知识数据库和模仿动物行为。在 21 世纪的头几十年，高度数学的统计机器学习已经主导了该领域，并且该技术已被证明非常成功，有助于解决整个工业界和学术界的许多具有挑战性的问题。<ref name="AI widely used">AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201</ref>

1956年AI作为一门学科被建立起来，后来经历过几段乐观时期<ref name="Simon 1965">Simon, H. A. (1965). The Shape of Automation for Men and Management. New York: Harper & Row.</ref>, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109.</ref><ref name="McCorduck 2004">McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN 1-56881-205-1</ref><ref name="Crevier 1993">Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0-465-02997-3</ref><ref name="Russell & Norvig 2003"/><ref name="NRC 1999">NRC (United States National Research Council) (1999). "Developments in Artificial Intelligence". Funding a Revolution: Government Support for Computing Research. National Academy Press.</ref><ref name="Newquist 1994"/>与紧随而来的亏损以及缺乏资金的困境（也就是“AI寒冬”<ref name="Crevier 1993"/><ref name="Russell & Norvig 2003"/><ref name="NRC 1999"/><ref name="Howe 1994"/><ref name="Newquist 1994"/><ref name="McCorduck 2004"/>），每次又找到了新的出路，取得了新的成果和新的投资<ref name="Clark 2015b"/>。AI 研究在其一生中尝试并放弃了许多不同的方法，包括模拟大脑、模拟人类问题解决、形式逻辑、大型知识数据库和模仿动物行为。在 21 世纪的头几十年，高度数学的统计机器学习已经主导了该领域，并且该技术已被证明非常成功，有助于解决整个工业界和学术界的许多具有挑战性的问题。<ref name="Russell & Norvig 2003"/><ref name="Kurzweil 2005"/><ref name="NRC 1999"/><ref name="Newquist 1994"/>

AI 研究的各个子领域都围绕特定目标和特定工具的使用。人工智能研究的传统目标包括推理、知识表示、规划、学习、自然语言处理、感知以及移动和操纵物体的能力。<ref name="Problems of AI">This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998</ref>通用智能（解决任意问题的能力）是该领域的长期目标之一。<ref name="General intelligence"> General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005</ref>为了解决这些问题，人工智能研究人员使用了各种版本的搜索和数学优化、形式逻辑、人工神经网络以及基于统计的方法，概率和经济学。AI 还借鉴了计算机科学、心理学、语言学、哲学和许多其他领域。

AI 研究的各个子领域都围绕特定目标和特定工具的使用。人工智能研究的传统目标包括推理、知识表示、规划、学习、自然语言处理、感知以及移动和操纵物体的能力。<ref name="Russell & Norvig 2003"/> <ref name="Luger & Stubblefield 2004"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Nilsson 1998"/>通用智能（解决任意问题的能力）是该领域的长期目标之一。<ref name="Kurzweil 1999">Kurzweil, Ray (1999). The Age of Spiritual Machines. Penguin Books. ISBN 978-0-670-88217-5.</ref><ref name="Kurzweil 2005">Kurzweil, Ray (2005). The Singularity is Near. Penguin Books. ISBN 978-0-670-03384-3.</ref>为了解决这些问题，人工智能研究人员使用了各种版本的搜索和数学优化、形式逻辑、人工神经网络以及基于统计的方法，概率和经济学。AI 还借鉴了计算机科学、心理学、语言学、哲学和许多其他领域。

这一领域是建立在人类智能“可以被精确描述从而使机器可以模拟”的观点上的。这一观点引出了关于思维的本质和造具有类人智能AI的伦理方面的哲学争论，于是自古以来<ref name="Newquist 1994">Newquist, HP (1994). The Brain Makers: Genius, Ego, And Greed In The Quest For Machines That Think. New York: Macmillan/SAMS. ISBN 978-0-672-30412-5.</ref>就有一些神话、小说以及哲学对此类问题展开过探讨。一些人认为AI的发展不会威胁人类生存；<ref>Spadafora, Anthony (21 October 2016). "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. Archived from the original on 28 August 2017.</ref><ref>Lombardo, P; Boehm, I; Nairz, K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078.</ref>但另一些人认为AI与以前的技术革命不同，它将带来大规模失业的风险。<ref name="guardian jobs debate">Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018.</ref>

这一领域是建立在人类智能“可以被精确描述从而使机器可以模拟”的观点上的。这一观点引出了关于思维的本质和造具有类人智能AI的伦理方面的哲学争论，于是自古以来<ref name="Newquist 1994"/>就有一些神话、小说以及哲学对此类问题展开过探讨。一些人认为AI的发展不会威胁人类生存；<ref>Spadafora, Anthony (21 October 2016). "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. Archived from the original on 28 August 2017.</ref><ref>Lombardo, P; Boehm, I; Nairz, K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078.</ref>但另一些人认为AI与以前的技术革命不同，它将带来大规模失业的风险。<ref name="guardian jobs debate">Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018.</ref>

具有思维能力的人造生物在古代以故事讲述者的方式出现，<ref name="AI in myth"/> 在小说中也很常见。比如 Mary Shelley的《弗兰肯斯坦 Frankenstein 》和 Karel Čapek的《罗素姆的万能机器人 Rossum's Universal Robots，R.U.R.》<ref name="AI in early science fiction">AI in early science fiction. * McCorduck 2004, pp. 17–25</ref> ——小说中的角色和他们的命运向人们提出了许多现在在人工智能伦理学中讨论的同样的问题。

具有思维能力的人造生物在古代以故事讲述者的方式出现，<ref name="AI in myth"/> 在小说中也很常见。比如 Mary Shelley的《弗兰肯斯坦 Frankenstein 》和 Karel Čapek的《罗素姆的万能机器人 Rossum's Universal Robots，R.U.R.》 <ref name="McCorduck 2004"/> ——小说中的角色和他们的命运向人们提出了许多现在在人工智能伦理学中讨论的同样的问题。

机械化或者说“形式化”推理的研究始于古代的哲学家和数学家。这些数理逻辑的研究直接催生了图灵的计算理论，即机器可以通过移动如“0”和“1”的简单的符号，就能模拟任何通过数学推演可以想到的过程，这一观点被称为'''邱奇-图灵论题 Church–Turing Thesis'''<ref name="Formal reasoning">Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020.</ref>。图灵提出“如果人类无法区分机器和人类的回应，那么机器可以被认为是“智能的”。</ref>{{Citation | last = Turing | first = Alan | authorlink=Alan Turing | year=1948 | chapter=Machine Intelligence | title = The Essential Turing: The ideas that gave birth to the computer age | editor=Copeland, B. Jack | isbn = 978-0-19-825080-7 | publisher = Oxford University Press | location = Oxford | page = 412 }}</ref>目前人们公认的最早的AI工作是由McCullouch和Pitts 在1943年正式设计的图灵完备“人工神经元”。<ref>Russell, Stuart J.; Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 978-0-13-604259-4..</ref>

机械化或者说“形式化”推理的研究始于古代的哲学家和数学家。这些数理逻辑的研究直接催生了图灵的计算理论，即机器可以通过移动如“0”和“1”的简单的符号，就能模拟任何通过数学推演可以想到的过程，这一观点被称为'''邱奇-图灵论题 Church–Turing Thesis'''<ref name="Formal reasoning">Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682.</ref>。图灵提出“如果人类无法区分机器和人类的回应，那么机器可以被认为是“智能的”。</ref>{{Citation | last = Turing | first = Alan | authorlink=Alan Turing | year=1948 | chapter=Machine Intelligence | title = The Essential Turing: The ideas that gave birth to the computer age | editor=Copeland, B. Jack | isbn = 978-0-19-825080-7 | publisher = Oxford University Press | location = Oxford | page = 412 }}</ref>目前人们公认的最早的AI工作是由McCullouch和Pitts 在1943年正式设计的图灵完备“人工神经元”。<ref>Russell, Stuart J.; Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 978-0-13-604259-4..</ref>

AI研究于1956年起源于在达特茅斯学院举办的一个研讨会，<ref>Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201</ref>其中术语“人工智能”是由约翰麦卡锡创造的，目的是将该领域与控制论区分开来，并摆脱控制论主义者诺伯特维纳的影响。<ref>McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73</ref>与会者Allen Newell(CMU)，[[赫伯特·西蒙 Herbert Simon]](CMU)，[[约翰·麦卡锡 John McCarthy]](MIT)，[[马文•明斯基 Marvin Minsky]](MIT)和[[阿瑟·塞缪尔Arthur Samuel]](IBM)成为了AI研究的创始人和领导者。他们和他们的学生做了一个新闻表述为“叹为观止”的计算机学习策略(以及在1959年就被报道达到人类的平均水平之上) ，解决代数应用题，证明逻辑理论以及用英语进行表达。到20世纪60年代中期，美国国防高级研究计划局斥重资支持研究，世界各地纷纷建立研究室。AI的创始人对未来充满乐观: Herbert Simon预测“二十年内，机器将能完成人能做到的一切工作。”。Marvin Minsky对此表示同意，他写道: “在一代人的时间里... ... 创造‘AI’的问题将得到实质性的解决。”

AI研究于1956年起源于在达特茅斯学院举办的一个研讨会，<ref name="McCorduck 2004"/><ref name="Crevier 1993"/> <ref name="Russell & Norvig 2003"/><ref name="NRC 1999"/>其中术语“人工智能”是由约翰麦卡锡创造的，目的是将该领域与控制论区分开来，并摆脱控制论主义者诺伯特维纳的影响。<ref>McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73</ref>与会者Allen Newell(CMU)，[[赫伯特·西蒙 Herbert Simon]](CMU)，[[约翰·麦卡锡 John McCarthy]](MIT)，[[马文•明斯基 Marvin Minsky]](MIT)和[[阿瑟·塞缪尔Arthur Samuel]](IBM)成为了AI研究的创始人和领导者。他们和他们的学生做了一个新闻表述为“叹为观止”的计算机学习策略(以及在1959年就被报道达到人类的平均水平之上) ，解决代数应用题，证明逻辑理论以及用英语进行表达。到20世纪60年代中期，美国国防高级研究计划局斥重资支持研究，世界各地纷纷建立研究室。AI的创始人对未来充满乐观: Herbert Simon预测“二十年内，机器将能完成人能做到的一切工作。”。Marvin Minsky对此表示同意，他写道: “在一代人的时间里... ... 创造‘AI’的问题将得到实质性的解决。”

在20世纪80年代初期，由于专家系统在商业上取得的成功，AI研究迎来了复兴，<ref name="Expert systems"> Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183</ref>专家系统是一种能够模拟人类专家的知识和分析能力的程序。到1985年，AI市场超过了10亿美元。与此同时，日本的第五代计算机项目促使了美国和英国政府恢复对学术研究的资助。<ref name="AI in the 80s"/> 然而，随着1987年 Lisp 机器市场的崩溃，AI再一次遭遇低谷，并陷入了第二次持续更长时间的停滞。<ref name="Second AI winter"/>

在20世纪80年代初期，由于专家系统在商业上取得的成功，AI研究迎来了复兴，<ref name="Expert systems"><ref name="ACM 1998"/><ref name="Russell & Norvig 2003"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/><ref name="McCorduck 2004"/><ref name="Crevier 1993"/><ref name="Newquist 1994"/>专家系统是一种能够模拟人类专家的知识和分析能力的程序。到1985年，AI市场超过了10亿美元。与此同时，日本的第五代计算机项目促使了美国和英国政府恢复对学术研究的资助。<ref name="AI in the 80s"/> 然而，随着1987年 Lisp 机器市场的崩溃，AI再一次遭遇低谷，并陷入了第二次持续更长时间的停滞。<ref name="Second AI winter"/>

人工智能在 1990 年代末和 21 世纪初通过寻找特定问题的具体解决方案，例如物流、数据挖掘或医疗诊断，逐渐恢复了声誉。到 2000 年，人工智能解决方案被广泛应用于幕后。<ref name="AI widely used" /> 狭窄的焦点使研究人员能够产生可验证的结果，开发更多的数学方法，并与其他领域（如统计学、经济学和数学）合作。<ref name="Formal methods in AI" >Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487</ref>

人工智能在 1990 年代末和 21 世纪初通过寻找特定问题的具体解决方案，例如物流、数据挖掘或医疗诊断，逐渐恢复了声誉。到 2000 年，人工智能解决方案被广泛应用于幕后。<ref name="AI widely used" /> 狭窄的焦点使研究人员能够产生可验证的结果，开发更多的数学方法，并与其他领域（如统计学、经济学和数学）合作。<ref name="Russell & Norvig 2003"/><ref name="McCorduck 2004"/>

=== 推理，解决问题===

=== 推理，解决问题===

早期的研究人员开发了一种算法，这种算法模仿了人类在解决谜题或进行逻辑推理时所使用的循序渐进的推理。<ref name="Reasoning">Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12</ref>到20世纪80年代末和90年代，AI研究使用概率论和经济学的理论开发出了处理不确定或不完全信息的方法。<ref name="Uncertain reasoning">Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19</ref>

早期的研究人员开发了一种算法，这种算法模仿了人类在解决谜题或进行逻辑推理时所使用的循序渐进的推理。<ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/>到20世纪80年代末和90年代，AI研究使用概率论和经济学的理论开发出了处理不确定或不完全信息的方法。<ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/> <ref name="Nilsson 1998"/>

这些算法被证明不足以解决大型推理问题，因为它们经历了一个“组合爆炸” : 随着问题规模变得越来越大，它们的处理效率呈指数级下降。<ref name="Intractability"> Intractability and efficiency and the combinatorial explosion: * Russell & Norvig 2003, pp. 9, 21–22</ref>事实上，即使是人类也很少使用早期AI研究建模的逐步推理。人们通过快速、直觉的判断来解决大多数问题。<ref name="Psychological evidence of sub-symbolic reasoning">Wason, P. C.; Shapiro, D. (1966). "Reasoning". In Foss, B. M. (ed.). New horizons in psychology. Harmondsworth: Penguin. Archived from the original on 26 July 2020. Retrieved 18 November 2019.</ref>

这些算法被证明不足以解决大型推理问题，因为它们经历了一个“组合爆炸” : 随着问题规模变得越来越大，它们的处理效率呈指数级下降。<ref name="Russell & Norvig 2003"/>事实上，即使是人类也很少使用早期AI研究建模的逐步推理。人们通过快速、直觉的判断来解决大多数问题。<ref name="Psychological evidence of sub-symbolic reasoning">Wason, P. C.; Shapiro, D. (1966). "Reasoning". In Foss, B. M. (ed.). New horizons in psychology. Harmondsworth: Penguin. Archived from the original on 26 July 2020. Retrieved 18 November 2019.</ref>

=== 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'''上本体 Upper Ontology''',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="ACM 1998"/><ref name="Russell & Norvig 2003"/> <ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/>和'''知识工程 Knowledge Engineering'''<ref name="Knowledge engineering">Knowledge engineering:<ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Nilsson 1998"/>。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): <ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/></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): <ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Nilsson 1998"/></ref>原因和结果;<ref name="Poole, Mackworth & Goebel 1998"/>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'''上本体 Upper Ontology''',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>

'''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 name="Poole, Mackworth & Goebel 1998">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 name="Luger & Stubblefield 2004">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 name="Nilsson 1998">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>

[[File:Hierarchical-control-system.svg|thumb| 分层控制系统是控制系统的一种形式，在这种控制系统中，一组设备和控制软件被放在一个层次结构中.]]

[[File:Hierarchical-control-system.svg|thumb| 分层控制系统是控制系统的一种形式，在这种控制系统中，一组设备和控制软件被放在一个层次结构中.]]

智能体必须能够设定并实现目标。<ref>ACM Computing Classification System: Artificial intelligence". ACM. 1998.</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, pp. 375–459</ref><ref>Poole, David; Mackworth, Alan; Goebel, Randy (1998). Computational Intelligence: A Logical Approach. New York: Oxford University Press. ISBN 978-0-19-510270-3. pp. 281–316</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, pp. 314–329</ref><ref>Nilsson, Nils (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978-1-55860-467-4, chpt. 10.1–2, 22</ref>他们需要能够有设想未来的办法——这是一种对其所处环境状况的表述，并能够预测他们的行动将如何改变环境——依此能够选择使效用（或者“价值”）最大化的选项。<ref name="Information value theory">Information value theory: * Russell & Norvig 2003, pp. 600–604</ref>

智能体必须能够设定并实现目标。<ref name="ACM 1998">ACM Computing Classification System: Artificial intelligence". ACM. 1998.</ref><ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/>他们需要能够有设想未来的办法——这是一种对其所处环境状况的表述，并能够预测他们的行动将如何改变环境——依此能够选择使效用（或者“价值”）最大化的选项。<ref name="Russell & Norvig 2003"/>

===学习===

===学习===

'''机器学习 Machine Learning（ML）'''是自AI诞生以来就有的一个基本概念，它研究如何通过经验自动改进计算机算法。

'''机器学习 Machine Learning（ML）'''是自AI诞生以来就有的一个基本概念，它研究如何通过经验自动改进计算机算法。<ref name="ACM 1998"/><ref name="Russell & Norvig 2003">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, pp. 375–459</ref><ref name="Poole, Mackworth & Goebel 1998"/><ref name="Luger & Stubblefield 2004"/><ref name="Nilsson 1998"/>

   

   

[[Unsupervised learning]] is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. [[Supervised learning]] includes both [[statistical classification|classification]] and numerical [[Regression analysis|regression]], which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.<ref name="Machine learning"/> Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". [[Computational learning theory]] can assess learners by [[computational complexity]], by [[sample complexity]] (how much data is required), or by other notions of [[optimization theory|optimization]].<ref>{{cite journal|last1=Jordan|first1=M. I.|last2=Mitchell|first2=T. M.|title=Machine learning: Trends, perspectives, and prospects|journal=Science|date=16 July 2015|volume=349|issue=6245|pages=255–260|doi=10.1126/science.aaa8415|pmid=26185243|bibcode=2015Sci...349..255J}}</ref> In [[reinforcement learning]]<ref name="Reinforcement learning"/> the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

'''无监督学习 Unsupervised Learning'''可以从数据流中发现某种模式，而不需要人类提前标注数据。'''有监督学习 Supervised Learning'''包括分类和回归，这需要人类首先标注数据。分类被用于确定某物属于哪个类别，这需要把大量来自多个类别的例子投入程序；回归用来产生一个描述输入和输出之间的关系的函数，并预测输出会如何随着输入的变化而变化<ref name="Machine learning"/><ref>{{cite journal|last1=Jordan|first1=M. I.|last2=Mitchell|first2=T. M.|title=Machine learning: Trends, perspectives, and prospects|journal=Science|date=16 July 2015|volume=349|issue=6245|pages=255–260|doi=10.1126/science.aaa8415|pmid=26185243|bibcode=2015Sci...349..255J}}</ref>。在强化学习<ref name="Russell & Norvig 2003"/><ref name="Luger & Stubblefield 2004"/>中，智能体会因为好的回应而受到奖励，因为坏的回应而受到惩罚；智能体通过一系列的奖励和惩罚形成了一个在其问题空间中可施行的策略。

===自然语言处理===

===自然语言处理===

[[File:ParseTree.svg|thumb|一个代表了语法根据一些句子的结构形式文法的解析树。]]

[[File:ParseTree.svg|thumb|一个代表了语法根据一些句子的结构形式文法的解析树。]]

 

'''自然语言处理 Natural language processing（NLP）'''赋予机器阅读和理解人类语言的能力。一个足够强大的自然语言处理系统可以提供自然语言用户界面，并能直接从如新闻专线文本的人类文字中获取知识。一些简单的自然语言处理的应用包括信息检索、文本挖掘、问答和机器翻译。<ref name="Russell & Norvig 2003"/><ref name="Luger & Stubblefield 2004"/>目前许多方法使用词的共现频率来构建文本的句法表示。用“关键词定位”策略进行搜索很常见且可扩展，但很粗糙;搜索“狗”可能只匹配与含“狗”字的文档，而漏掉与“犬”匹配的文档。“词汇相关性”策略使用如“事故”这样的词出现的频次，评估文本想表达的情感。现代统计NLP方法可以结合所有这些策略以及其他策略，在以页或段落为单位的处理上获得还能让人接受的准确度，但仍然缺乏对单独的句子进行分类所需的语义理解。除了编码语义常识常见的困难外，现有的语义NLP有时可扩展性太差，无法应用到在商业中。而“叙述性”NLP除了达到语义NLP的功能之外，还想最终能做到充分理解常识推理。<ref>Cambria, Erik; White, Bebo (May 2014). "Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]". IEEE Computational Intelligence Magazine. 9 (2): 48–57. doi:10.1109/MCI.2014.2307227. S2CID 206451986.</ref>到2019年，变压器基于深度学习的架构可以生成连贯的文本。<ref>Vincent, James (7 November 2019). "OpenAI has published the text-generating AI it said was too dangerous to share". The Verge. Archived from the original on 11 June 2020. </ref>

[[Natural language processing]]<ref name="Natural language processing"/> (NLP) gives machines the ability to read and [[natural language understanding|understand]] human language. A sufficiently powerful natural language processing system would enable [[natural-language user interface]]s and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include [[information retrieval]], [[text mining]], [[question answering]]<ref>[https://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis "Versatile question answering systems: seeing in synthesis"] {{webarchive|url=https://web.archive.org/web/20160201125047/http://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis |date=1 February 2016 }}, Mittal et al., IJIIDS, 5(2), 119–142, 2011

 

自然语言处理(NLP)<ref name="Natural language processing"/>赋予机器阅读和理解人类语言的能力。一个足够强大的自然语言处理系统可以提供自然语言用户界面，并能直接从如新闻专线文本的人类文字中获取知识。一些简单的自然语言处理的应用包括信息检索、文本挖掘、问答和机器翻译<ref>[https://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis "Versatile question answering systems: seeing in synthesis"] {{webarchive|url=https://web.archive.org/web/20160201125047/http://www.academia.edu/2475776/Versatile_question_answering_systems_seeing_in_synthesis |date=1 February 2016 }}, Mittal et al., IJIIDS, 5(2), 119–142, 2011。目前许多方法使用词的共现频率来构建文本的句法表示。用“关键词定位”策略进行搜索很常见且可扩展，但很粗糙;搜索“狗”可能只匹配与含“狗”字的文档，而漏掉与“犬”匹配的文档。“词汇相关性”策略使用如“事故”这样的词出现的频次，评估文本想表达的情感。现代统计NLP方法可以结合所有这些策略以及其他策略，在以页或段落为单位的处理上获得还能让人接受的准确度，但仍然缺乏对单独的句子进行分类所需的语义理解。除了编码语义常识常见的困难外，现有的语义NLP有时可扩展性太差，无法应用到在商业中。而“叙述性”NLP除了达到语义NLP的功能之外，还想最终能做到充分理解常识推理。

[[File:Ääretuvastuse näide.png|thumb|特征检测（如图：边缘检测）帮助人工智能从原始数据中组合出信息丰富的抽象结构]]

[[File:Ääretuvastuse näide.png|thumb|特征检测（如图：边缘检测）帮助人工智能从原始数据中组合出信息丰富的抽象结构]]

 

'''机器感知 Machine perception'''<ref name="Russell & Norvig 2003"/><ref name="Nilsson 1998"/>是利用传感器(如可见光或红外线摄像头、麦克风、无线信号、激光雷达、声纳、雷达和触觉传感器)的输入来推断世界的不同角度的能力。应用包括语音识别、面部识别和物体识别。<ref name="ACM 1998"/><ref name="Russell & Norvig 2003"/>计算机视觉是分析可视化输入的能力。这种输入通常是模糊的; 一个在远处50米高的巨人可能会与近处正常大小的行人占据完全相同的像素，这就要求AI判断不同解释的相对可能性和合理性，例如使用”物体模型”来判断50米高的巨人其实是不存在的。<ref name="Nilsson 1998"/><ref name="ACM 1998"/><ref name="Russell & Norvig 2003"/>

[Machine perception]]<ref name="Machine perception"/> is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active [[lidar]], sonar, radar, and [[tactile sensor]]s) to deduce aspects of the world. Applications include [[speech recognition]],<ref name="Speech recognition"/> [[facial recognition system|facial recognition]], and [[object recognition]].<ref name="Object recognition"/> [[Computer vision]] is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.<ref name="Computer vision"/>

 

 

机器感知是利用传感器(如可见光或红外线摄像头、麦克风、无线信号、激光雷达、声纳<ref name="Speech recognition"/>、雷达和触觉传感器)的输入来推断世界的不同角度的能力。应用包括语音识别、面部识别和物体识别。计算机视觉<ref name="Object recognition"/> 是分析可视化输入的能力。这种输入通常是模糊的; 一个在远处50米高的巨人可能会与近处正常大小的行人占据完全相同的像素，这就要求AI判断不同解释的相对可能性和合理性，例如使用”物体模型”来判断50米高的巨人其实是不存在的。<ref name="Computer vision"/>

=== 运动和操作 ===

=== 运动和操作 ===

AI在机器人学中应用广泛<ref name="Robotics"/>。在现代工厂中广泛使用的高级机械臂和其他工业机器人，可以从经验中学习如何在存在摩擦和齿轮滑移的情况下有效地移动。当处在一个静态且可见的小环境中时，现代移动机器人<ref name="Configuration space"/>可以很容易地确定自己的位置并绘制环境地图；然而如果是动态环境，比如用内窥镜检查病人呼吸的身体的内部，难度就会更高。运动规划是将一个运动任务分解为如单个的关节运动这样的“基本任务”的过程。这种运动通常包括顺应运动，在这个过程中需要与物体保持物理接触。'''莫拉维克悖论 Moravec's Paradox''' <ref name="Robotic mapping"/><ref>{{cite journal|last1=Cadena|first1=Cesar|last2=Carlone|first2=Luca|last3=Carrillo|first3=Henry|last4=Latif|first4=Yasir|last5=Scaramuzza|first5=Davide|last6=Neira|first6=Jose|last7=Reid|first7=Ian|last8=Leonard|first8=John J.|title=Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age|journal=IEEE Transactions on Robotics|date=December 2016|volume=32|issue=6|pages=1309–1332|doi=10.1109/TRO.2016.2624754|arxiv=1606.05830|bibcode=2016arXiv160605830C}}</ref>概括了人类理所当然认为低水平的感知运动技能很难在编程给机器人的事实，这个悖论是以汉斯 · 莫拉维克的名字命名的，他在1988年表示: “让计算机在智力测试或下跳棋中展现出成人水平的表现相对容易，但要让计算机拥有一岁小孩的感知和移动能力却很难，甚至不可能。”<ref>{{Cite book| first = Hans | last = Moravec | year = 1988 | title = Mind Children | publisher = Harvard University Press | author-link =Hans Moravec| p=15}}</ref><ref>{{cite news|last1=Chan|first1=Szu Ping|title=This is what will happen when robots take over the world|url=https://www.telegraph.co.uk/finance/economics/11994694/Heres-what-will-happen-when-robots-take-over-the-world.html|accessdate=23 April 2018|date=15 November 2015}}</ref>这是因为，身体灵巧性在数百万年的自然选择中一直作为一个直接的目标以增强人类的生存能力；而与此相比，跳棋技能则很奢侈，“擅长跳棋”的基因并不被生存导向的自然选择所偏好与富集。<ref name="The Economist">{{cite news|title=IKEA furniture and the limits of AI|url=https://www.economist.com/news/leaders/21740735-humans-have-had-good-run-most-recent-breakthrough-robotics-it-clear|accessdate=24 April 2018|work=The Economist|date=2018|language=en}}</ref>

AI在机器人学中应用广泛<ref name="ACM 1998"/><ref name="Russell & Norvig 2003"/><ref name="Poole, Mackworth & Goebel 1998"/>。在现代工厂中广泛使用的高级机械臂和其他工业机器人，可以从经验中学习如何在存在摩擦和齿轮滑移的情况下有效地移动。<ref name="Russell & Norvig 2003"/>当处在一个静态且可见的小环境中时，现代移动机器人可以很容易地确定自己的位置并绘制环境地图；然而如果是动态环境，比如用内窥镜检查病人呼吸的身体的内部，难度就会更高。运动规划是将一个运动任务分解为如单个的关节运动这样的“基本任务”的过程。这种运动通常包括顺应运动，在这个过程中需要与物体保持物理接触。<ref name="Tecuci 2012">Tecuci, Gheorghe (March–April 2012). "Artificial Intelligence". Wiley Interdisciplinary Reviews: Computational Statistics. 4 (2): 168–180. doi:10.1002/wics.200</ref><ref name="Russell & Norvig 2003"/><ref>Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. Bibcode:2016arXiv160605830C. doi:10.1109/TRO.2016.2624754. S2CID 2596787</ref>'''莫拉维克悖论 Moravec's Paradox''' <ref name="Robotic mapping"/><ref>{{cite journal|last1=Cadena|first1=Cesar|last2=Carlone|first2=Luca|last3=Carrillo|first3=Henry|last4=Latif|first4=Yasir|last5=Scaramuzza|first5=Davide|last6=Neira|first6=Jose|last7=Reid|first7=Ian|last8=Leonard|first8=John J.|title=Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age|journal=IEEE Transactions on Robotics|date=December 2016|volume=32|issue=6|pages=1309–1332|doi=10.1109/TRO.2016.2624754|arxiv=1606.05830|bibcode=2016arXiv160605830C}}</ref>概括了人类理所当然认为低水平的感知运动技能很难在编程给机器人的事实，这个悖论是以汉斯 · 莫拉维克的名字命名的，他在1988年表示: “让计算机在智力测试或下跳棋中展现出成人水平的表现相对容易，但要让计算机拥有一岁小孩的感知和移动能力却很难，甚至不可能。”<ref>{{Cite book| first = Hans | last = Moravec | year = 1988 | title = Mind Children | publisher = Harvard University Press | author-link =Hans Moravec| p=15}}</ref><ref>{{cite news|last1=Chan|first1=Szu Ping|title=This is what will happen when robots take over the world|url=https://www.telegraph.co.uk/finance/economics/11994694/Heres-what-will-happen-when-robots-take-over-the-world.html|accessdate=23 April 2018|date=15 November 2015}}</ref>这是因为，身体灵巧性在数百万年的自然选择中一直作为一个直接的目标以增强人类的生存能力；而与此相比，跳棋技能则很奢侈，“擅长跳棋”的基因并不被生存导向的自然选择所偏好与富集。<ref name="The Economist">{{cite news|title=IKEA furniture and the limits of AI|url=https://www.economist.com/news/leaders/21740735-humans-have-had-good-run-most-recent-breakthrough-robotics-it-clear|accessdate=24 April 2018|work=The Economist|date=2018|language=en}}</ref>

[[File:Kismet robot at MIT Museum.jpg|thumb|Kismet，一个具有基本社交技能的机器人]]

[[File:Kismet robot at MIT Museum.jpg|thumb|Kismet，一个具有基本社交技能的机器人]]

Moravec's paradox can be extended to many forms of social intelligence.<ref>{{cite magazine |last1=Thompson|first1=Derek|title=What Jobs Will the Robots Take?|url=https://www.theatlantic.com/business/archive/2014/01/what-jobs-will-the-robots-take/283239/|accessdate=24 April 2018|magazine=The Atlantic|date=2018}}</ref><ref>{{cite journal|last1=Scassellati|first1=Brian|title=Theory of mind for a humanoid robot|journal=Autonomous Robots|volume=12|issue=1|year=2002|pages=13–24|doi=10.1023/A:1013298507114}}</ref> Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.<ref>{{cite journal|last1=Cao|first1=Yongcan|last2=Yu|first2=Wenwu|last3=Ren|first3=Wei|last4=Chen|first4=Guanrong|title=An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination|journal=IEEE Transactions on Industrial Informatics|date=February 2013|volume=9|issue=1|pages=427–438|doi=10.1109/TII.2012.2219061|arxiv=1207.3231}}</ref> [[Affective computing]] is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human [[Affect (psychology)|affects]].{{sfn|Thro|1993}}{{sfn|Edelson|1991}}{{sfn|Tao|Tan|2005}} Moderate successes related to affective computing include textual [[sentiment analysis]] and, more recently, multimodal affect analysis (see [[multimodal sentiment analysis]]), wherein AI classifies the affects displayed by a videotaped subject.<ref>{{cite journal|last1=Poria|first1=Soujanya|last2=Cambria|first2=Erik|last3=Bajpai|first3=Rajiv|last4=Hussain|first4=Amir|title=A review of affective computing: From unimodal analysis to multimodal fusion|journal=Information Fusion|date=September 2017|volume=37|pages=98–125|doi=10.1016/j.inffus.2017.02.003|hdl=1893/25490|hdl-access=free}}</ref>

'''情感计算 Affective computing'''是一个跨学科交叉领域，包括了识别、解释、处理、模拟人的情感的系统。例如，一些虚拟助手被编程为对话式说话，甚至幽默地开玩笑；它使他们对人类互动的情感动态更加敏感，或者以其他方式促进人机交互。<ref name="Minsky 2006">Minsky, Marvin (2006). The Emotion Machine. New York, NY: Simon & Schusterl. ISBN 978-0-7432-7663-4.</ref>然而，这往往会给幼稚的用户一个不切实际的概念，即现有的计算机代理实际上有多智能。<ref>Waddell, Kaveh (2018). "Chatbots Have Entered the Uncanny Valley". The Atlantic. </ref>与情感计算相关的一些还算成功的领域有文本情感分析，以及最近的'''多模态情感分析 Multimodal Affect Analysis''' <ref>{{cite journal|last1=Cao|first1=Yongcan|last2=Yu|first2=Wenwu|last3=Ren|first3=Wei|last4=Chen|first4=Guanrong|title=An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination|journal=IEEE Transactions on Industrial Informatics|date=February 2013|volume=9|issue=1|pages=427–438|doi=10.1109/TII.2012.2219061|arxiv=1207.3231}}</ref>，多模态情感分析中AI{{sfn|Thro|1993}}{{sfn|Edelson|1991}}{{sfn|Tao|Tan|2005}}可以做到将录像中被试表现出的情感进行分类。<ref>{{cite journal|last1=Poria|first1=Soujanya|last2=Cambria|first2=Erik|last3=Bajpai|first3=Rajiv|last4=Hussain|first4=Amir|title=A review of affective computing: From unimodal analysis to multimodal fusion|journal=Information Fusion|date=September 2017|volume=37|pages=98–125|doi=10.1016/j.inffus.2017.02.003|hdl=1893/25490|hdl-access=free}}</ref>

=== 通用智能 ===

=== 通用智能 ===

Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese [[Fifth generation computer|Fifth Generation Computer Systems]] initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation).<ref name="contemporary agi">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref> Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with [[artificial general intelligence]] (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.<ref name="General intelligence"/><ref name="Roberts">{{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}</ref> Many advances have general, cross-domain significance. One high-profile example is that [[DeepMind]] in the 2010s developed a "generalized artificial intelligence" that could learn many diverse [[Atari 2600|Atari]] games on its own, and later developed a variant of the system which succeeds at [[Catastrophic interference#The Sequential Learning Problem: McCloskey and Cohen (1989)|sequential learning]].<ref>{{cite news|title=The superhero of artificial intelligence: can this genius keep it in check?|url=https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligence-deepmind-alphago|accessdate=26 April 2018|work=the Guardian|date=16 February 2016|language=en}}</ref><ref>{{cite journal|last1=Mnih|first1=Volodymyr|last2=Kavukcuoglu|first2=Koray|last3=Silver|first3=David|last4=Rusu|first4=Andrei A.|last5=Veness|first5=Joel|last6=Bellemare|first6=Marc G.|last7=Graves|first7=Alex|last8=Riedmiller|first8=Martin|last9=Fidjeland|first9=Andreas K.|last10=Ostrovski|first10=Georg|last11=Petersen|first11=Stig|last12=Beattie|first12=Charles|last13=Sadik|first13=Amir|last14=Antonoglou|first14=Ioannis|last15=King|first15=Helen|last16=Kumaran|first16=Dharshan|last17=Wierstra|first17=Daan|last18=Legg|first18=Shane|last19=Hassabis|first19=Demis|title=Human-level control through deep reinforcement learning|journal=Nature|date=26 February 2015|volume=518|issue=7540|pages=529–533|doi=10.1038/nature14236|pmid=25719670|bibcode=2015Natur.518..529M}}</ref><ref>{{cite news|last1=Sample|first1=Ian|title=Google's DeepMind makes AI program that can learn like a human|url=https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-like-a-human|accessdate=26 April 2018|work=the Guardian|date=14 March 2017|language=en}}</ref> Besides [[transfer learning]],<ref>{{cite news|title=From not working to neural networking|url=https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not|accessdate=26 April 2018|work=The Economist|date=2016|language=en}}</ref> hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured [[World Wide Web|Web]].{{sfn|Russell|Norvig|2009|chapter=27. AI: The Present and Future}} Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI.{{sfn|Domingos|2015|chapter=9. The Pieces of the Puzzle Fall into Place}} Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that [[anthropomorphism|anthropomorphic]] features like an [[artificial brain]] or simulated [[developmental robotics|child development]] may someday reach a critical point where general intelligence emerges.<ref name="Brain simulation"/><ref>{{cite journal|last1=Goertzel|first1=Ben|last2=Lian|first2=Ruiting|last3=Arel|first3=Itamar|last4=de Garis|first4=Hugo|last5=Chen|first5=Shuo|title=A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures|journal=Neurocomputing|date=December 2010|volume=74|issue=1–3|pages=30–49|doi=10.1016/j.neucom.2010.08.012}}</ref>

Historically, projects such as the Cyc knowledge base (1984–) and the massive Japanese [[Fifth generation computer|Fifth Generation Computer Systems]] initiative (1982–1992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation).<ref name="contemporary agi">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref> Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with [[artificial general intelligence]] (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.<ref name="General intelligence"/><ref name="Roberts">{{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}</ref> Many advances have general, cross-domain significance. One high-profile example is that [[DeepMind]] in the 2010s developed a "generalized artificial intelligence" that could learn many diverse [[Atari 2600|Atari]] games on its own, and later developed a variant of the system which succeeds at [[Catastrophic interference#The Sequential Learning Problem: McCloskey and Cohen (1989)|sequential learning]].<ref>{{cite news|title=The superhero of artificial intelligence: can this genius keep it in check?|url=https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligence-deepmind-alphago|accessdate=26 April 2018|work=the Guardian|date=16 February 2016|language=en}}</ref><ref>{{cite journal|last1=Mnih|first1=Volodymyr|last2=Kavukcuoglu|first2=Koray|last3=Silver|first3=David|last4=Rusu|first4=Andrei A.|last5=Veness|first5=Joel|last6=Bellemare|first6=Marc G.|last7=Graves|first7=Alex|last8=Riedmiller|first8=Martin|last9=Fidjeland|first9=Andreas K.|last10=Ostrovski|first10=Georg|last11=Petersen|first11=Stig|last12=Beattie|first12=Charles|last13=Sadik|first13=Amir|last14=Antonoglou|first14=Ioannis|last15=King|first15=Helen|last16=Kumaran|first16=Dharshan|last17=Wierstra|first17=Daan|last18=Legg|first18=Shane|last19=Hassabis|first19=Demis|title=Human-level control through deep reinforcement learning|journal=Nature|date=26 February 2015|volume=518|issue=7540|pages=529–533|doi=10.1038/nature14236|pmid=25719670|bibcode=2015Natur.518..529M}}</ref><ref>{{cite news|last1=Sample|first1=Ian|title=Google's DeepMind makes AI program that can learn like a human|url=https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-like-a-human|accessdate=26 April 2018|work=the Guardian|date=14 March 2017|language=en}}</ref> Besides [[transfer learning]],<ref>{{cite news|title=From not working to neural networking|url=https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not|accessdate=26 April 2018|work=The Economist|date=2016|language=en}}</ref> hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured [[World Wide Web|Web]].{{sfn|Russell|Norvig|2009|chapter=27. AI: The Present and Future}} Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI.{{sfn|Domingos|2015|chapter=9. The Pieces of the Puzzle Fall into Place}} Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that [[anthropomorphism|anthropomorphic]] features like an [[artificial brain]] or simulated [[developmental robotics|child development]] may someday reach a critical point where general intelligence emerges.<ref name="Brain simulation"/><ref>{{cite journal|last1=Goertzel|first1=Ben|last2=Lian|first2=Ruiting|last3=Arel|first3=Itamar|last4=de Garis|first4=Hugo|last5=Chen|first5=Shuo|title=A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures|journal=Neurocomputing|date=December 2010|volume=74|issue=1–3|pages=30–49|doi=10.1016/j.neucom.2010.08.012}}</ref>

历史上，诸如 Cyc 知识库(1984 -)和大规模的日本第五代计算机系统倡议(1982-1992)等项目试图涵盖人类的所有认知。这些早期的项目未能逃脱非定量符号逻辑模型的限制，现在回过头看，这些项目大大低估了实现跨领域AI的难度。当下绝大多数AI研究人员主要研究易于处理的“狭义AI”应用(如医疗诊断或汽车导航)<ref name="contemporary agi">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref>。许多研究人员预测，不同领域的“狭义AI”工作最终将被整合到一台具有人工通用智能(AGI)的机器中，结合上文提到的大多数狭义功能，甚至在某种程度上在大多数或所有这些领域都超过人类。许多进展具有普遍的、跨领域的意义。<ref name="General intelligence"/><ref name="Roberts">{{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}</ref>一个著名的例子是，21世纪一零年代，DeepMind开发了一种“'''<font color=#ff8000>通用人工智能 Generalized Artificial Intelligence</font>'''” ，它可以自己学习许多不同的 Atari 游戏，后来又开发了这种系统的升级版，在序贯学习方面取得了成功。<ref>{{cite news|title=The superhero of artificial intelligence: can this genius keep it in check?|url=https://www.theguardian.com/technology/2016/feb/16/demis-hassabis-artificial-intelligence-deepmind-alphago|accessdate=26 April 2018|work=the Guardian|date=16 February 2016|language=en}}</ref><ref>{{cite journal|last1=Mnih|first1=Volodymyr|last2=Kavukcuoglu|first2=Koray|last3=Silver|first3=David|last4=Rusu|first4=Andrei A.|last5=Veness|first5=Joel|last6=Bellemare|first6=Marc G.|last7=Graves|first7=Alex|last8=Riedmiller|first8=Martin|last9=Fidjeland|first9=Andreas K.|last10=Ostrovski|first10=Georg|last11=Petersen|first11=Stig|last12=Beattie|first12=Charles|last13=Sadik|first13=Amir|last14=Antonoglou|first14=Ioannis|last15=King|first15=Helen|last16=Kumaran|first16=Dharshan|last17=Wierstra|first17=Daan|last18=Legg|first18=Shane|last19=Hassabis|first19=Demis|title=Human-level control through deep reinforcement learning|journal=Nature|date=26 February 2015|volume=518|issue=7540|pages=529–533|doi=10.1038/nature14236|pmid=25719670|bibcode=2015Natur.518..529M}}</ref><ref>{{cite news|last1=Sample|first1=Ian|title=Google's DeepMind makes AI program that can learn like a human|url=https://www.theguardian.com/global/2017/mar/14/googles-deepmind-makes-ai-program-that-can-learn-like-a-human|accessdate=26 April 2018|work=the Guardian|date=14 March 2017|language=en}}</ref>除了迁移学习，未来AGI <ref>{{cite news|title=From not working to neural networking|url=https://www.economist.com/news/special-report/21700756-artificial-intelligence-boom-based-old-idea-modern-twist-not|accessdate=26 April 2018|work=The Economist|date=2016|language=en}}</ref> 的突破可能包括开发能够进行决策理论元推理的反射架构，以及从整个非结构化的网页中整合一个全面的知识库。{{sfn|Russell|Norvig|2009|chapter=27. AI: The Present and Future}} 一些人认为，某种(目前尚未发现的)概念简单，但在数学上困难的“终极算法”可以产生AGI。最后，一些“涌现”的方法着眼于尽可能地模拟人类智能，并相信如人工大脑或模拟儿童发展等拟人方案，有一天会达到一个临界点，通用智能在此涌现。<ref name="Brain simulation"/><ref>{{cite journal|last1=Goertzel|first1=Ben|last2=Lian|first2=Ruiting|last3=Arel|first3=Itamar|last4=de Garis|first4=Hugo|last5=Chen|first5=Shuo|title=A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures|journal=Neurocomputing|date=December 2010|volume=74|issue=1–3|pages=30–49|doi=10.1016/j.neucom.2010.08.012}}</ref>

历史上，诸如 Cyc 知识库(1984 -)和大规模的日本第五代计算机系统倡议(1982-1992)等项目试图涵盖人类的所有认知。这些早期的项目未能逃脱非定量符号逻辑模型的限制，现在回过头看，这些项目大大低估了实现跨领域AI的难度。当下绝大多数AI研究人员主要研究易于处理的“狭义AI”应用(如医疗诊断或汽车导航)<ref name="contemporary agi">{{cite book|last1=Pennachin|first1=C.|last2=Goertzel|first2=B.|title=Contemporary Approaches to Artificial General Intelligence|journal=Artificial General Intelligence. Cognitive Technologies|date=2007|doi=10.1007/978-3-540-68677-4_1|publisher=Springer|location=Berlin, Heidelberg|series=Cognitive Technologies|isbn=978-3-540-23733-4}}</ref>。许多研究人员预测，不同领域的“狭义AI”工作最终将被整合到一台具有人工通用智能(AGI)的机器中，结合上文提到的大多数狭义功能，甚至在某种程度上在大多数或所有这些领域都超过人类。<ref name="Kurzweil 1999"/><ref name="Kurzweil 2005"/><ref name="Roberts">{{cite magazine|last1=Roberts|first1=Jacob|title=Thinking Machines: The Search for Artificial Intelligence|magazine=Distillations|date=2016|volume=2|issue=2|pages=14–23|url=https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|accessdate=20 March 2018|archive-url=https://web.archive.org/web/20180819152455/https://www.sciencehistory.org/distillations/magazine/thinking-machines-the-search-for-artificial-intelligence|archive-date=19 August 2018|url-status=dead}}</ref>

 

 

 

 

== 方法 ==

== 方法 ==

一些作品向我们展示了有感知的能力，因此也有遭受苦难的能力的AI，迫使我们面对是什么让我们成为人类这一根本问题。这些都在卡雷尔 · 阿佩克的电影《人工智能》、《机器姬》，以及菲利普·K·迪克的小说《机器人会梦见电子羊吗?》中都有出现。迪克认为，AI创造的技术改变了我们对人类主观性的理解。<ref>{{Cite journal|last=Galvan|first=Jill|date=1 January 1997|title=Entering the Posthuman Collective in Philip K. Dick's "Do Androids Dream of Electric Sheep?"|journal=Science Fiction Studies|volume=24|issue=3|pages=413–429|jstor=4240644}}</ref>

一些作品向我们展示了有感知的能力，因此也有遭受苦难的能力的AI，迫使我们面对是什么让我们成为人类这一根本问题。这些都在卡雷尔 · 阿佩克的电影《人工智能》、《机器姬》，以及菲利普·K·迪克的小说《机器人会梦见电子羊吗?》中都有出现。迪克认为，AI创造的技术改变了我们对人类主观性的理解。<ref>{{Cite journal|last=Galvan|first=Jill|date=1 January 1997|title=Entering the Posthuman Collective in Philip K. Dick's "Do Androids Dream of Electric Sheep?"|journal=Science Fiction Studies|volume=24|issue=3|pages=413–429|jstor=4240644}}</ref>

==参见 See also==

==参见==

 

 

 

 

{{portal|Computer programming}}

{{portal|Computer programming}}

{{col div|colwidth=20em}}

{{col div|colwidth=20em}}

* [[溯因推理 Abductive reasoning]]

* [[溯因推理 Abductive reasoning]]

 

* [[爱,死亡,机器人 A.I. Rising]]

 

 

* ''[[爱,死亡,机器人 A.I. Rising]]''

 

 

 

* [[人工智能武器装备竞赛 Artificial intelligence arms race]]

* [[人工智能武器装备竞赛 Artificial intelligence arms race]]

* [[行为选择算法 Behavior selection algorithm]]

* [[行为选择算法 Behavior selection algorithm]]

* [[交易处理自动机 Business process automation]]

* [[交易处理自动机 Business process automation]]

 

* [[基于案例的推理 Case-based reasoning]]

 

 

* [[基于案例的推理 Case-based reasoning]]

 

 

 

* [[常识推理 Commonsense reasoning]]

* [[常识推理 Commonsense reasoning]]

* [[涌现算法 Emergent algorithm]]

* [[涌现算法 Emergent algorithm]]

* [[进化计算 Evolutionary computation]]

* [[进化计算 Evolutionary computation]]

* [[人工智能技术的女性 Female gendering of AI technologies]]

* [[人工智能技术的女性 Female gendering of AI technologies]]

* [[人工智能术语表 Glossary of artificial intelligence]]

* [[人工智能术语表 Glossary of artificial intelligence]]

 

=== 人工智能教科书 ===

{{refbegin|30em}}

 

* {{cite book  

 

| last=Hutter |first=Marcus |author-link=Marcus Hutter |year=2005

 

| title=Universal Artificial Intelligence

| isbn=978-3-540-22139-5

 

| publisher=Springer

 

| location=Berlin

 

| title-link=AIXI }}

* {{cite book

 

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|first=Philip

 

|author-link=Philip C. Jackson, Jr.

|year=1985

 

|title=Introduction to Artificial Intelligence

 

|isbn=978-0-486-24864-6

 

|publisher=Dover

|edition=2nd

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

=== 人工智能教科书 ===

{{refbegin|30em}}

* {{cite book  

| last=Hutter |first=Marcus |author-link=Marcus Hutter |year=2005

| title=Universal Artificial Intelligence

| isbn=978-3-540-22139-5

| publisher=Springer

| location=Berlin

| title-link=AIXI }}

* {{cite book

|last=Jackson

|first=Philip

|author-link=Philip C. Jackson, Jr.

|year=1985

|title=Introduction to Artificial Intelligence

|isbn=978-0-486-24864-6

|publisher=Dover

|edition=2nd

|url-access=registration

|url-access=registration

|url=https://archive.org/details/introductiontoar1985jack

|publisher=Morgan Kaufmann

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|access-date=18 November 2019

|archive-date=26 July 2020

|archive-date=26 July 2020

|archive-url=https://web.archive.org/web/20200726131713/https://archive.org/details/introductiontoar1985jack

|archive-url=https://web.archive.org/web/20200726131654/https://archive.org/details/artificialintell0000nils

|url-status=live

|url-status=live

}}

}}

* {{cite book

* {{cite book

|last1=Luger

|first1      = David

|first1=George

|last1        = Poole

|author-link=George Luger

|author-link = David Poole (researcher)

|last2=Stubblefield

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|first2=William

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|author2-link=William Stubblefield

|author2-link = Alan Mackworth

|year=2004

|title=Artificial Intelligence: Structures and Strategies for Complex Problem Solving

|last3        = Goebel

|publisher=Benjamin/Cummings

|author3-link = Randy Goebel

|edition=5th

|year         = 1998

|isbn=978-0-8053-4780-7

|title       = Computational Intelligence: A Logical Approach

|url=https://archive.org/details/artificialintell0000luge

|publisher   = Oxford University Press

|location    = New York

|access-date=17 December 2019

|isbn         = 978-0-19-510270-3

|archive-date=26 July 2020

|url         = https://archive.org/details/computationalint00pool

|archive-url=https://web.archive.org/web/20200726220613/https://archive.org/details/artificialintell0000luge

|access-date = 22 August 2020

|url-status=live

|archive-date = 26 July 2020

|archive-url = https://web.archive.org/web/20200726131436/https://archive.org/details/computationalint00pool

|url-status   = live

}}

}}

* {{cite book |last1=Neapolitan |first1=Richard |last2=Jiang |first2=Xia |year=2018 |author-link1=Richard Neapolitan |title=Artificial Intelligence: With an Introduction to Machine Learning |publisher=Chapman & Hall/CRC |isbn=978-1-138-50238-3 |url=https://www.crcpress.com/Contemporary-Artificial-Intelligence-Second-Edition/Neapolitan-Jiang/p/book/9781138502383 |access-date=3 January 2018 |archive-date=22 August 2020 |archive-url=https://web.archive.org/web/20200822201555/https://www.routledge.com/Contemporary-Artificial-Intelligence-Second-Edition/Neapolitan-Jiang/p/book/9781138502383 |url-status=live }}

* {{cite book | last=Winston | first=Patrick Henry | author-link=Patrick Winston | year=1984 | title=Artificial Intelligence | publisher=Addison-Wesley | location=Reading, MA | isbn=978-0-201-08259-3 | url=https://archive.org/details/artificialintell00wins | access-date=22 August 2020 | archive-date=26 July 2020 | archive-url=https://web.archive.org/web/20200726131500/https://archive.org/details/artificialintell00wins | url-status=live }}

* {{cite book |last=Rich |first=Elaine |author-link=Elaine Rich |year=1983 |title=Artificial Intelligence |publisher=McGraw-Hill |isbn=978-0-07-052261-9 |url-access=registration |url=https://archive.org/details/ine0000unse |access-date=17 December 2019 |archive-date=26 July 2020 |archive-url=https://web.archive.org/web/20200726131632/https://archive.org/details/ine0000unse |url-status=live }}

* {{cite book

* {{cite book

|last=Nilsson

| last=Bundy |first=Alan |author-link=Alan Bundy |year=1980

|first=Nils

| title=Artificial Intelligence: An Introductory Course

|author-link=Nils Nilsson (researcher)

| publisher = Edinburgh University Press|edition=2nd

|year=1998

| isbn=978-0-85224-410-4

|title=Artificial Intelligence: A New Synthesis

|url=https://archive.org/details/artificialintell0000nils

|url-access=registration

|isbn=978-1-55860-467-4

}}

}}

* {{cite book

* {{cite book

|first1=David

|first1=David

|last1=Poole

|last1=Poole

|author-link=David Poole (researcher)

|author-link=David Poole (researcher)

* [[Adam Tooze|Tooze, Adam]], "Democracy and Its Discontents", ''[[The New York Review of Books]]'', vol. LXVI, no. 10 (6 June 2019), pp.&nbsp;52–53, 56–57.  "Democracy has no clear answer for the mindless operation of [[bureaucracy|bureaucratic]] and [[technology|technological power]].  We may indeed be witnessing its extension in the form of artificial intelligence and robotics.  Likewise, after decades of dire warning, the [[environmentalism|environmental problem]] remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour:  [[corporation]]s and the technologies they promote."  (pp.&nbsp;56–57.)

* [[Adam Tooze|Tooze, Adam]], "Democracy and Its Discontents", ''[[The New York Review of Books]]'', vol. LXVI, no. 10 (6 June 2019), pp.&nbsp;52–53, 56–57.  "Democracy has no clear answer for the mindless operation of [[bureaucracy|bureaucratic]] and [[technology|technological power]].  We may indeed be witnessing its extension in the form of artificial intelligence and robotics.  Likewise, after decades of dire warning, the [[environmentalism|environmental problem]] remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour:  [[corporation]]s and the technologies they promote."  (pp.&nbsp;56–57.)

{{refend}}

{{refend}}