7,129
个编辑
更改
跳到导航
跳到搜索
第154行：
第154行： − {{Main|Symbolic AI}}+ − − When access to digital computers became possible in the mid-1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: [[Carnegie Mellon University]], [[Stanford]] and [[MIT]], and as described below, each one developed its own style of research. [[John Haugeland]] named these symbolic approaches to AI "good old fashioned AI" or "[[GOFAI]]".<ref name="GOFAI"/> During the 1960s, symbolic approaches had achieved great success at simulating high-level "thinking" in small demonstration programs. Approaches based on [[cybernetics]] or [[artificial neural network]]s were abandoned or pushed into the background.<ref>The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of [[perceptron]]s by [[Marvin Minsky]] and [[Seymour Papert]] in 1969. See [[History of AI]], [[AI winter]], or [[Frank Rosenblatt]].</ref> − − 20世纪50年代中期，当数字计算机成为可能时，AI研究开始探索把人类智能化归为符号操纵的可能性。这项研究集中在3个机构: 卡内基梅隆大学，斯坦福和麻省理工学院，正如下面所描述的，每个机构都有自己的研究风格。约翰 · 豪格兰德将这些具有象征意义的AI方法命名为“好的老式人工智能 Good Old Fashioned AI”或“ GOFAI”<ref name="GOFAI"/>。20世纪60年代的时候，符号化方法在模拟高层次“思考”的小型程序中取得了巨大的成就。而基于[[控制论]]和[[人工神经网络]]的方法则被抛弃，或者只作为背景出现。<ref>The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of [[perceptron]]s by [[Marvin Minsky]] and [[Seymour Papert]] in 1969. See [[History of AI]], [[AI winter]], or [[Frank Rosenblatt]].</ref> − − − 20世纪六七十年代的研究人员相信，符号方法最终会成功地创造出一台具有[[通用人工智能]]的机器，并以此作为他们研究领域的目标。 第167行：
第160行： − + − Economist [[Herbert A. Simon|Herbert Simon]] and [[Allen Newell]] studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as [[cognitive science]], [[operations research]] and [[management science]]. Their research team used the results of [[psychology|psychological]] experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at [[Carnegie Mellon University]] would eventually culminate in the development of the [[Soar (cognitive architecture)|Soar]] architecture in the middle 1980s.<ref name="AI at CMU in the 60s"/><ref name="Soar"/> − − − 经济学家[[赫伯特·西蒙]]和[[艾伦·纽厄尔]]研究了人类解决问题的技能，并试图将其形式化。他们的工作为AI、认知科学、运筹学和管理科学奠定了基础。他们的研究团队利用心理学实验的结果来开发程序，模拟人们用来解决问题的方法。以卡内基梅隆大学为中心，这种研究传统最终在20世纪80年代中期的SOAR架构开发过程中达到顶峰。<ref name="AI at CMU in the 60s"/><ref name="Soar"/> − Unlike Simon and Newell, [[John McCarthy (computer scientist)|John McCarthy]] felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms.<ref name="Biological intelligence vs. intelligence in general"/> His laboratory at [[Stanford University|Stanford]] ([[Stanford Artificial Intelligence Laboratory|SAIL]]) focused on using formal [[logic]] to solve a wide variety of problems, including [[knowledge representation]], [[automated planning and scheduling|planning]] and [[machine learning|learning]].<ref name="AI at Stanford in the 60s"/> Logic was also the focus of the work at the [[University of Edinburgh]] and elsewhere in Europe which led to the development of the programming language [[Prolog]] and the science of [[logic programming]].<ref name="AI at Edinburgh and France in the 60s"/>+ − − − 与西蒙和纽厄尔不同，约翰·麦卡锡认为机器不需要模拟人类的思维，而是应该尝试寻找抽象推理和解决问题的本质，不管人们是否使用相同的算法。<ref name="Biological intelligence vs. intelligence in general"/> 他在斯坦福大学的实验室(SAIL)致力于使用形式逻辑来解决各种各样的问题，包括知识表示、规划和学习<ref name="AI at Stanford in the 60s"/> 。逻辑也是爱丁堡大学和欧洲其他地方工作的重点，这促进了编程语言 Prolog 和逻辑编程科学的发展。 − Researchers at [[MIT]] (such as [[Marvin Minsky]] and [[Seymour Papert]])<ref name="AI at MIT in the 60s"/> found that solving difficult problems in [[computer vision|vision]] and [[natural language processing]] required ad-hoc solutions—they argued that there was no simple and general principle (like [[logic]]) that would capture all the aspects of intelligent behavior. [[Roger Schank]] described their "anti-logic" approaches as "[[Neats vs. scruffies|scruffy]]" (as opposed to the "[[neats vs. scruffies|neat]]" paradigms at [[Carnegie Mellon University|CMU]] and Stanford).<ref name="Neats vs. scruffies"/> [[Commonsense knowledge bases]] (such as [[Doug Lenat]]'s [[Cyc]]) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.<ref name="Cyc"/>+ − − 麻省理工学院(MIT)的研究人员马文•明斯基和西摩•派珀特等发现<ref name="AI at MIT in the 60s"/>，视觉和自然语言处理中的难题需要特定的解决方案——他们认为，没有简单而普遍的原则(如逻辑)可以涵盖智能行为。罗杰•尚克将他们的“反逻辑”方法形容为“邋遢的”(相对于卡内基梅隆大学和斯坦福大学的“整洁”范式)。常识库(如常识知识库的 Cyc)是“邋遢”AI的一个例子，因为它们必须人工一个一个地构建复杂概念。 − + − When computers with large memories became available around 1970, researchers from all three traditions began to build [[knowledge representation|knowledge]] into AI applications.<ref name="Knowledge revolution"/> This "knowledge revolution" led to the development and deployment of [[expert system]]s (introduced by [[Edward Feigenbaum]]), the first truly successful form of AI software.<ref name="Expert systems"/> A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.<ref>{{Cite journal |last=Frederick |first=Hayes-Roth |last2=William |first2=Murray |last3=Leonard |first3=Adelman |title=Expert systems|journal=AccessScience |language=en |doi=10.1036/1097-8542.248550}}</ref> The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications. − − 1970年左右，当拥有大容量存储器的计算机出现时，来自这三个研究方向的研究人员开始将知识应用于AI领域<ref name="Knowledge revolution"/> 。这一轮“知识革命”的一大成果是开发和部署专家系统，第一个真正成功的AI软件<ref name="Expert systems"/>。所有专家系统的一个关键部件是存储着事实和规则的知识库<ref>{{Cite journal |last=Frederick |first=Hayes-Roth |last2=William |first2=Murray |last3=Leonard |first3=Adelman |title=Expert systems|journal=AccessScience |language=en |doi=10.1036/1097-8542.248550}}</ref>。推动知识革命的另一个原因是人们认识到，许多简单的AI应用程序也需要大量的知识。 − − By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially [[machine perception|perception]], [[robotics]], [[machine learning|learning]] and [[pattern recognition]]. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.<ref name="Symbolic vs. sub-symbolic"/> Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.+ − − − 到了20世纪80年代，符号AI的进步似乎停滞不前，许多人认为符号系统永远无法模仿人类认知的所有过程，尤其在感知、机器人学、学习和模式识别等方面。许多研究人员开始研究针对特定AI问题的“亚符号”方法<ref name="Symbolic vs. sub-symbolic"/>。亚符号方法能在没有特定知识表示的情况下，做到接近智能。 + − This includes [[embodied agent|embodied]], [[situated]], [[behavior-based AI|behavior-based]], and [[nouvelle AI]]. Researchers from the related field of [[robotics]], such as [[Rodney Brooks]], rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.<ref name="Embodied AI"/> Their work revived the non-symbolic point of view of the early [[cybernetic]]s researchers of the 1950s and reintroduced the use of [[control theory]] in AI. This coincided with the development of the [[embodied mind thesis]] in the related field of [[cognitive science]]: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence. − − − '''具身智慧 Embodied Intelligence'''包括具体化的、情境化的、基于行为的新式 AI。来自机器人相关领域的研究人员，如罗德尼·布鲁克斯，放弃了符号化AI的方法，而专注于使机器人能够移动和生存的基本工程问题<ref name="Embodied AI"/>。他们的工作重启了20世纪50年代早期控制论研究者的非符号观点，并将控制论重新引入到AI的应用中。这与认知科学相关领域的具身理论的发展相吻合: 认为如运动、感知和视觉等身体的各个功能是高智能所必需的。 − + − − Within [[developmental robotics]], developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).{{sfn|Weng|McClelland|Pentland|Sporns|2001}}{{sfn|Lungarella|Metta|Pfeifer|Sandini|2003}}{{sfn|Asada|Hosoda|Kuniyoshi|Ishiguro|2009}}{{sfn|Oudeyer|2010}} − − − Interest in [[Artificial neural network|neural networks]] and "[[connectionism]]" was revived by [[David Rumelhart]] and others in the middle of the 1980s.<ref name="Revival of connectionism"/> [[Artificial neural network]]s are an example of [[soft computing]]—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other [[soft computing]] approaches to AI include [[fuzzy system]]s, [[Grey system theory]], [[evolutionary computation]] and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of [[computational intelligence]].<ref name="Computational intelligence"/>+ − − − 上世纪80年代中期，大卫•鲁梅尔哈特等人重新激发了人们对神经网络和“'''连接主义 Connectionism'''”的兴趣。人工神经网络是软计算的一个例子ーー它们解决不能完全用逻辑确定性地解决，但常常只需要近似解的问题。AI的其他软计算方法包括'''模糊系统 Fuzzy Systems '''、'''灰度系统理论 Grey System Theory'''、'''演化计算 Evolutionary Computation '''和许多统计工具。软计算在AI中的应用是计算智能这一新兴学科的集中研究领域。<ref name="Computational intelligence"/> − − Much of traditional [[Symbolic artificial intelligence|GOFAI]] got bogged down on ''ad hoc'' patches to [[symbolic computation]] that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as [[hidden Markov model]]s (HMM), [[information theory]], and normative Bayesian [[decision theory]] to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like [[mathematics]], economics or [[operations research]]).{{efn|While such a "victory of the neats" may be a consequence of the field becoming more mature, [[Artificial Intelligence: A Modern Approach|AIMA]] states that in practice both [[neats and scruffies|neat and scruffy]] approaches continue to be necessary in AI research.}} Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as [[data mining]], without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more [[scientific method|scientific]]. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.<ref name="Formal methods in AI"/><ref>{{cite news|last1=Hutson|first1=Matthew|title=Artificial intelligence faces reproducibility crisis|url=http://science.sciencemag.org/content/359/6377/725|accessdate=28 April 2018|work=[[Science Magazine|Science]]|date=16 February 2018|pages=725–726|language=en|doi=10.1126/science.359.6377.725|bibcode=2018Sci...359..725H}}</ref> Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language.{{sfn|Norvig|2012}} Critics note that the shift from GOFAI to statistical learning is often also a shift away from [[explainable AI]]. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.{{sfn|Langley|2011}}{{sfn|Katz|2012}}+ − − − 许多传统的 GOFAI 在实验模型中行之有效，但不能推广到现实世界，陷入了需要不断给符号计算修补漏洞的困境中。然而，在20世纪90年代前后，AI研究人员采用了复杂的数学工具，如'''<font color=#ff8000>隐马尔可夫模型 Hidden Markov Model,HMM</font>'''、信息论和'''<font color=#ff8000>标准贝叶斯决策理论 Normative Bayesian Decision Theory</font>'''来比较或统一各种互相竞争的架构。共通的数学语言允许其与数学、经济学或运筹学等更成熟的领域进行高层次的融合。与 GOFAI 相比，隐马尔可夫模型和神经网络等新的“统计学习”技术在数据挖掘等许多实际领域中不必理解数据集的语义，却能得到更高的精度，随着现实世界数据的日益增加，人们越来越注重用不同的方法测试相同的数据，并进行比较，看哪种方法在比特殊实验室环境更广泛的背景下表现得更好; AI研究正变得更加科学。如今，实验结果一般是严格可测的，有时可以重现（但有难度）<ref name="Formal methods in AI"/><ref>{{cite news|last1=Hutson|first1=Matthew|title=Artificial intelligence faces reproducibility crisis|url=http://science.sciencemag.org/content/359/6377/725|accessdate=28 April 2018|work=[[Science Magazine|Science]]|date=16 February 2018|pages=725–726|language=en|doi=10.1126/science.359.6377.725|bibcode=2018Sci...359..725H}}</ref> 。不同的统计学习技术有不同的局限性，例如，基本的 HMM 不能为自然语言的无限可能的组合建模。评论者们指出，从 GOFAI 到统计学习的转变也经常是可解释AI的转变。在 [[通用人工智能]] 的研究中，{{sfn|Norvig|2012}}一些学者警告不要过度依赖统计学习，并认为继续研究 GOFAI 仍然是实现通用智能的必要条件。{{sfn|Langley|2011}}{{sfn|Katz|2012}} − ;Intelligent agent paradigm: An [[intelligent agent]] is a system that perceives its environment and takes actions that maximize its chances of success. The simplest intelligent agents are programs that solve specific problems. More complicated agents include human beings and organizations of human beings (such as [[firm]]s). The paradigm allows researchers to directly compare or even combine different approaches to isolated problems, by asking which agent is best at maximizing a given "goal function". An agent that solves a specific problem can use any approach that works—some agents are symbolic and logical, some are sub-symbolic [[artificial neural network]]s and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as [[decision theory]] and economics—that also use concepts of abstract agents. Building a complete agent requires researchers to address realistic problems of integration; for example, because sensory systems give uncertain information about the environment, planning systems must be able to function in the presence of uncertainty. The intelligent agent paradigm became widely accepted during the 1990s.<ref name="Intelligent agents"/>+ − − − − − ;[[Agent architecture]]s and [[cognitive architecture]]s:Researchers have designed systems to build intelligent systems out of interacting [[intelligent agent]]s in a [[multi-agent system]].<ref name="Agent architectures"/> A [[hierarchical control system]] provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modeling.<ref name="Hierarchical control system"/> Some cognitive architectures are custom-built to solve a narrow problem; others, such as [[Soar (cognitive architecture)|Soar]], are designed to mimic human cognition and to provide insight into general intelligence. Modern extensions of Soar are [[hybrid intelligent system]]s that include both symbolic and sub-symbolic components.<ref>{{cite journal|last1=Laird|first1=John|title=Extending the Soar cognitive architecture|journal=Frontiers in Artificial Intelligence and Applications|date=2008|volume=171|page=224|citeseerx=10.1.1.77.2473}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Lebiere|first2=Christian|last3=Oltramari|first3=Alessandro|title=The knowledge level in cognitive architectures: Current limitations and possibile developments|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=39–55|doi=10.1016/j.cogsys.2017.05.001|hdl=2318/1665207|hdl-access=free}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Bhatt|first2=Mehul|last3=Oltramari|first3=Alessandro|last4=Vernon|first4=David|title=The role of cognitive architectures in general artificial intelligence|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=1–3|doi=10.1016/j.cogsys.2017.08.003|hdl=2318/1665249|hdl-access=free}}</ref>+ − 智能主体体系结构和认知体系结构: 研究人员已经设计了一些在多智能体系统中利用相互作用的智能体构建智能系统的系统<ref name="Agent architectures"/>。分层控制系统为亚符号AI、反应层和符号AI提供了一座桥梁，亚符号AI在底层、反应层和符号AI在顶层<ref name="Hierarchical control system"/>。+ − 一些认知架构是人为构造用来解决特定问题的；其他比如SOAR，是用来模仿人类的认知，向通用智能更进一步。现在SOAR的扩展是含有符号和亚符号部分的混合智能系统。<ref>{{cite journal|last1=Laird|first1=John|title=Extending the Soar cognitive architecture|journal=Frontiers in Artificial Intelligence and Applications|date=2008|volume=171|page=224|citeseerx=10.1.1.77.2473}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Lebiere|first2=Christian|last3=Oltramari|first3=Alessandro|title=The knowledge level in cognitive architectures: Current limitations and possibile developments|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=39–55|doi=10.1016/j.cogsys.2017.05.001|hdl=2318/1665207|hdl-access=free}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Bhatt|first2=Mehul|last3=Oltramari|first3=Alessandro|last4=Vernon|first4=David|title=The role of cognitive architectures in general artificial intelligence|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=1–3|doi=10.1016/j.cogsys.2017.08.003|hdl=2318/1665249|hdl-access=free}}</ref>
→方法
===符号化方法===
===符号化方法===
当 1950 年代中期可以使用数字计算机时，人工智能研究开始探索将人类智能简化为符号操作的可能性。该研究集中在三个机构进行：卡内基梅隆大学、斯坦福大学和麻省理工学院，如下所述，每个机构都发展了自己的研究风格。John Haugeland将这些象征性的人工智能方法命名为“优秀的老式人工智能”或“ GOFAI ”。<ref name="GOFAI">Haugeland, John (1985). Artificial Intelligence: The Very Idea. Cambridge, Mass.: MIT Press. ISBN 978-0-262-08153-5.</ref>在 1960 年代，符号方法在小型演示程序中模拟高级“思维”方面取得了巨大成功，到 1980 年代，它在专家系统方面取得了巨大成功。基于控制论或人工神经网络的方法被放弃或推到了后台。1960 年代和 1970 年代的研究人员确信，符号方法最终会成功地创造出具有通用人工智能的机器，并认为这是他们领域的目标。
====模拟认知的方法====
====模拟认知的方法====
经济学家[[Herbert A. Simon]]和[[Allen Newell]]研究了人类解决问题的技能，并试图将其形式化。他们的工作为AI、认知科学、运筹学和管理科学奠定了基础。他们的研究团队利用心理学实验的结果来开发程序，模拟人们用来解决问题的方法。以卡内基梅隆大学为中心，这种研究传统最终在20世纪80年代中期的SOAR架构开发过程中达到顶峰。
==== 基于逻辑的方法 ====
==== 基于逻辑的方法 ====
与Simon和Newell不同，John McCarthy认为机器不需要模拟人类的思维，而是应该尝试寻找抽象推理和解决问题的本质，不管人们是否使用相同的算法。他在斯坦福大学的实验室(SAIL)致力于使用形式逻辑来解决各种各样的问题，包括知识表示、规划和学习。逻辑也是爱丁堡大学和欧洲其他地方工作的重点，这促进了编程语言 Prolog 和逻辑编程科学的发展。
====反逻辑的或“邋遢”的方法====
====反逻辑的或“邋遢”的方法====
麻省理工学院(MIT)的研究人员Marvin Minsky和Seymour Papert等发现，视觉和自然语言处理中的难题需要特定的解决方案——他们认为，没有简单而普遍的原则(如逻辑)可以涵盖智能行为。罗杰•尚克将他们的“反逻辑”方法形容为“邋遢的”(相对于卡内基梅隆大学和斯坦福大学的“整洁”范式)。常识库(如常识知识库的 Cyc)是“邋遢”AI的一个例子，因为它们必须人工一个一个地构建复杂概念。
====基于知识的方法====
====基于知识的方法====
1970年左右，当拥有大容量存储器的计算机出现时，来自这三个研究方向的研究人员开始将知识应用于AI领域。这一轮“知识革命”的一大成果是开发和部署专家系统，第一个真正成功的AI软件。所有专家系统的一个关键部件是存储着事实和规则的知识库<ref>{{Cite journal |last=Frederick |first=Hayes-Roth |last2=William |first2=Murray |last3=Leonard |first3=Adelman |title=Expert systems|journal=AccessScience |language=en |doi=10.1036/1097-8542.248550}}</ref>。推动知识革命的另一个原因是人们认识到，许多简单的AI应用程序也需要大量的知识。
===亚符号方法 ===
===亚符号方法 ===
到了20世纪80年代，符号AI的进步似乎停滞不前，许多人认为符号系统永远无法模仿人类认知的所有过程，尤其在感知、机器人学、学习和模式识别等方面。许多研究人员开始研究针对特定AI问题的“亚符号”方法。亚符号方法能在没有特定知识表示的情况下，做到接近智能。
====具身智慧====
====具身智慧====
'''具身智慧 Embodied Intelligence'''包括具体化的、情境化的、基于行为的新式 AI。来自机器人相关领域的研究人员，如Rodney Brooks，放弃了符号化AI的方法，而专注于使机器人能够移动和生存的基本工程问题<ref name="Embodied AI"/>。他们的工作重启了20世纪50年代早期控制论研究者的非符号观点，并将控制论重新引入到AI的应用中。这与认知科学相关领域的具身理论的发展相吻合: 认为如运动、感知和视觉等身体的各个功能是高智能所必需的。
在发展型机器人中，人们开发了发展型学习方法，通过自主的自我探索、与人类教师的社会互动，以及使用主动学习、成熟、协同运动等指导机制 ，使机器人积累新技能的能力。
在发展型机器人中，人们开发了发展型学习方法，通过自主的自我探索、与人类教师的社会互动，以及使用主动学习、成熟、协同运动等指导机制 ，使机器人积累新技能的能力。{{sfn|Weng|McClelland|Pentland|Sporns|2001}}{{sfn|Lungarella|Metta|Pfeifer|Sandini|2003}}{{sfn|Asada|Hosoda|Kuniyoshi|Ishiguro|2009}}{{sfn|Oudeyer|2010}}
====计算智能与软计算 ====
====计算智能与软计算 ====
上世纪80年代中期，David Rumelhart等人重新激发了人们对神经网络和“'''连接主义 Connectionism'''”的兴趣。人工神经网络是软计算的一个例子——它们解决不能完全用逻辑确定性地解决，但常常只需要近似解的问题。AI的其他软计算方法包括'''模糊系统 Fuzzy Systems '''、'''灰度系统理论 Grey System Theory'''、'''演化计算 Evolutionary Computation '''和许多统计工具。软计算在AI中的应用是计算智能这一新兴学科的集中研究领域。
===统计学习 ===
===统计学习 ===
许多传统的 GOFAI 在实验模型中行之有效，但不能推广到现实世界，陷入了需要不断给符号计算修补漏洞的困境中。然而，在20世纪90年代前后，AI研究人员采用了复杂的数学工具，如[[隐马尔可夫模型]] Hidden Markov Model（HMM）、信息论和标准贝叶斯决策理论 Normative Bayesian Decision Theory来比较或统一各种互相竞争的架构。共通的数学语言允许其与数学、经济学或运筹学等更成熟的领域进行高层次的融合。与 GOFAI 相比，隐马尔可夫模型和神经网络等新的“统计学习”技术在数据挖掘等许多实际领域中不必理解数据集的语义，却能得到更高的精度，随着现实世界数据的日益增加，人们越来越注重用不同的方法测试相同的数据，并进行比较，看哪种方法在比特殊实验室环境更广泛的背景下表现得更好; AI研究正变得更加科学。如今，实验结果一般是严格可测的，有时可以重现（但有难度）<ref name="Russell & Norvig 2003"/><ref name=" McCorduck 2004"/><ref name="Formal methods in AI"/><ref>{{cite news|last1=Hutson|first1=Matthew|title=Artificial intelligence faces reproducibility crisis|url=http://science.sciencemag.org/content/359/6377/725|accessdate=28 April 2018|work=[[Science Magazine|Science]]|date=16 February 2018|pages=725–726|language=en|doi=10.1126/science.359.6377.725|bibcode=2018Sci...359..725H}}</ref> 。不同的统计学习技术有不同的局限性，例如，基本的 HMM 不能为自然语言的无限可能的组合建模。评论者们指出，从 GOFAI 到统计学习的转变也经常是可解释AI的转变。在 [[通用人工智能]] 的研究中，<ref name="Norvig 2012"/>一些学者警告不要过度依赖统计学习，并认为继续研究 GOFAI 仍然是实现通用智能的必要条件。<ref name="Langley 2011"/><ref name="Katz 2012"/>
=== 集成各种方法 ===
=== 集成各种方法 ===
智能主体范式: 智能主体是一个感知其环境并采取行动，最大限度地提高其成功机会的系统。最简单的智能主体是解决特定问题的程序，更复杂的智能主体包括人类和人类组织(如公司)。这种范式使得研究人员能通过观察哪一个智能主体能最大化给定的“目标函数”，直接比较甚至结合不同的方法来解决孤立的问题。解决特定问题的智能主体可以使用任何有效的方法——可以是是符号化和逻辑化的，也可以是亚符号化的人工神经网络，还可以是新的方法。这种范式还为研究人员提供了一种与其他领域(如决策理论和经济学)进行交流的共同语言，因为这些领域也使用了抽象智能主体的概念。建立一个完整的智能主体需要研究人员解决现实的整合协调问题; 例如，由于传感系统提供关于环境的信息不确定，决策系统就必须在不确定性的条件下运作。智能体范式在20世纪90年代被广泛接受。
智能主体范式: 智能主体是一个感知其环境并采取行动，最大限度地提高其成功机会的系统。最简单的智能主体是解决特定问题的程序，更复杂的智能主体包括人类和人类组织(如公司)。这种范式使得研究人员能通过观察哪一个智能主体能最大化给定的“目标函数”，直接比较甚至结合不同的方法来解决孤立的问题。解决特定问题的智能主体可以使用任何有效的方法——可以是是符号化和逻辑化的，也可以是亚符号化的人工神经网络，还可以是新的方法。这种范式还为研究人员提供了一种与其他领域(如决策理论和经济学)进行交流的共同语言，因为这些领域也使用了抽象智能主体的概念。建立一个完整的智能主体需要研究人员解决现实的整合协调问题; 例如，由于传感系统提供关于环境的信息不确定，决策系统就必须在不确定性的条件下运作。智能体范式在20世纪90年代被广泛接受。<ref name="Intelligent agents"/>
智能主体体系结构和认知体系结构: 研究人员已经设计了一些在多智能体系统中利用相互作用的智能体构建智能系统的系统。分层控制系统为亚符号AI、反应层和符号AI提供了一座桥梁，亚符号AI在底层、反应层和符号AI在顶层<ref name="Hierarchical control system"/>。一些认知架构是人为构造用来解决特定问题的；其他比如SOAR，是用来模仿人类的认知，向通用智能更进一步。现在SOAR的扩展是含有符号和亚符号部分的混合智能系统。<ref>{{cite journal|last1=Laird|first1=John|title=Extending the Soar cognitive architecture|journal=Frontiers in Artificial Intelligence and Applications|date=2008|volume=171|page=224|citeseerx=10.1.1.77.2473}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Lebiere|first2=Christian|last3=Oltramari|first3=Alessandro|title=The knowledge level in cognitive architectures: Current limitations and possibile developments|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=39–55|doi=10.1016/j.cogsys.2017.05.001|hdl=2318/1665207|hdl-access=free}}</ref><ref>{{cite journal|last1=Lieto|first1=Antonio|last2=Bhatt|first2=Mehul|last3=Oltramari|first3=Alessandro|last4=Vernon|first4=David|title=The role of cognitive architectures in general artificial intelligence|journal=Cognitive Systems Research|date=May 2018|volume=48|pages=1–3|doi=10.1016/j.cogsys.2017.08.003|hdl=2318/1665249|hdl-access=free}}</ref>
<br>
==工具 ==
==工具 ==