计算机自学指南

『혼자 공부하는 컴퓨터구조 & 운영체제』 (한빛미디어)

本人自学计算机基础课程记录，主要为基础四大件，即大家常说的“408”，包含数据结构和算法 、计算机操作系统 、计算机网络 、计算机组成原理。学习资料来源王道课程，笔记插图来源于个人整理。

CS-self-learning program (https://github.com/pkuflyingpig/cs-self-learning/).

Complete self-learning materials of CS106L

This repo is used for archiving my notes, codes and materials of cs learning.

『혼자 공부하는 네트워크』 (한빛미디어)

Poole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. 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. 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 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 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." 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 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. 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. 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. 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 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 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. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. 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 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, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. 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 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. "Ask the AI experts: What's driving today's progress in AI?". McKinsey & Company. Archived from the original on 13 April 2018. Retrieved 13 April 2018. Administrator. "Kinect's AI breakthrough explained". i-programmer.info. Archived from the original on 1 February 2016. Rowinski, Dan (15 January 2013). "Virtual Personal Assistants & The Future Of Your Smartphone [Infographic]". ReadWrite. Archived from the original on 22 December 2015. "Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol". BBC News. 12 March 2016. Archived from the original on 26 August 2016. Retrieved 1 October 2016. Metz, Cade (27 May 2017). "After Win in China, AlphaGo's Designers Explore New AI". Wired. Archived from the original on 2 June 2017. "World's Go Player Ratings". May 2017. Archived from the original on 1 April 2017. "柯洁迎19岁生日 雄踞人类世界排名第一已两年" (in Chinese). May 2017. Archived from the original on 11 August 2017. Clark, Jack (8 December 2015). "Why 2015 Was a Breakthrough Year in Artificial Intelligence". Bloomberg News. Archived from the original on 23 November 2016. Retrieved 23 November 2016. After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever. "Reshaping Business With Artificial Intelligence". MIT Sloan Management Review. Archived from the original on 19 May 2018. Retrieved 2 May 2018. Lorica, Ben (18 December 2017). "The state of AI adoption". O'Reilly Media. Archived from the original on 2 May 2018. Retrieved 2 May 2018. Allen, Gregory (6 February 2019). "Understanding China's AI Strategy". Center for a New American Security. Archived from the original on 17 March 2019. "Review | How two AI superpowers – the U.S. and China – battle for supremacy in the field". Washington Post. 2 November 2018. Archived from the original on 4 November 2018. Retrieved 4 November 2018. at 10:11, Alistair Dabbs 22 Feb 2019. "Artificial Intelligence: You know it isn't real, yeah?". www.theregister.co.uk. Archived from the original on 21 May 2020. Retrieved 22 August 2020. "Stop Calling it Artificial Intelligence". Archived from the original on 2 December 2019. Retrieved 1 December 2019. "AI isn't taking over the world – it doesn't exist yet". GBG Global website. Archived from the original on 11 August 2020. Retrieved 22 August 2020. Kaplan, Andreas; Haenlein, Michael (1 January 2019). "Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence". Business Horizons. 62 (1): 15–25. doi:10.1016/j.bushor.2018.08.004. Domingos 2015, Chapter 5. Domingos 2015, Chapter 7. Lindenbaum, M., Markovitch, S., & Rusakov, D. (2004). Selective sampling for nearest neighbor classifiers. Machine learning, 54(2), 125–152. Domingos 2015, Chapter 1. Intractability and efficiency and the combinatorial explosion: * Russell & Norvig 2003, pp. 9, 21–22 Domingos 2015, Chapter 2, Chapter 3. Hart, P. E.; Nilsson, N. J.; Raphael, B. (1972). "Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"". SIGART Newsletter (37): 28–29. doi:10.1145/1056777.1056779. S2CID 6386648. Domingos 2015, Chapter 2, Chapter 4, Chapter 6. "Can neural network computers learn from experience, and if so, could they ever become what we would call 'smart'?". Scientific American. 2018. Archived from the original on 25 March 2018. Retrieved 24 March 2018. Domingos 2015, Chapter 6, Chapter 7. Domingos 2015, p. 286. "Single pixel change fools AI programs". BBC News. 3 November 2017. Archived from the original on 22 March 2018. Retrieved 12 March 2018. "AI Has a Hallucination Problem That's Proving Tough to Fix". WIRED. 2018. Archived from the original on 12 March 2018. Retrieved 12 March 2018. Matti, D.; Ekenel, H. K.; Thiran, J. P. (2017). Combining LiDAR space clustering and convolutional neural networks for pedestrian detection. 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). pp. 1–6. arXiv:1710.06160. doi:10.1109/AVSS.2017.8078512. ISBN 978-1-5386-2939-0. S2CID 2401976. Ferguson, Sarah; Luders, Brandon; Grande, Robert C.; How, Jonathan P. (2015). Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions. Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics. 107. Springer, Cham. pp. 161–177. arXiv:1405.5581. doi:10.1007/978-3-319-16595-0_10. ISBN 978-3-319-16594-3. S2CID 8681101. "Cultivating Common Sense | DiscoverMagazine.com". Discover Magazine. 2017. Archived from the original on 25 March 2018. Retrieved 24 March 2018. Davis, Ernest; Marcus, Gary (24 August 2015). "Commonsense reasoning and commonsense knowledge in artificial intelligence". Communications of the ACM. 58 (9): 92–103. doi:10.1145/2701413. S2CID 13583137. Archived from the original on 22 August 2020. Retrieved 6 April 2020. Winograd, Terry (January 1972). "Understanding natural language". Cognitive Psychology. 3 (1): 1–191. doi:10.1016/0010-0285(72)90002-3. "Don't worry: Autonomous cars aren't coming tomorrow (or next year)". Autoweek. 2016. Archived from the original on 25 March 2018. Retrieved 24 March 2018. Knight, Will (2017). "Boston may be famous for bad drivers, but it's the testing ground for a smarter self-driving car". MIT Technology Review. Archived from the original on 22 August 2020. Retrieved 27 March 2018. Prakken, Henry (31 August 2017). "On the problem of making autonomous vehicles conform to traffic law". Artificial Intelligence and Law. 25 (3): 341–363. doi:10.1007/s10506-017-9210-0. Lieto, Antonio (May 2018). "The knowledge level in cognitive architectures: Current limitations and possible developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. 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 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 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) 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 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 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 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 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence".[120] In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".[121] This is a form of Tom Mitchell's widely quoted definition of machine learning: "A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E." Learning: * ACM 1998, I.2.6, * Russell & Norvig 2003, pp. 649–788, * Poole, Mackworth & Goebel 1998, pp. 397–438, * Luger & Stubblefield 2004, pp. 385–542, * Nilsson 1998, chpt. 3.3, 10.3, 17.5, 20 Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260. Bibcode:2015Sci...349..255J. doi:10.1126/science.aaa8415. PMID 26185243. S2CID 677218. Reinforcement learning: * Russell & Norvig 2003, pp. 763–788 * Luger & Stubblefield 2004, pp. 442–449 Natural language processing: * ACM 1998, I.2.7 * Russell & Norvig 2003, pp. 790–831 * Poole, Mackworth & Goebel 1998, pp. 91–104 * Luger & Stubblefield 2004, pp. 591–632 "Versatile question answering systems: seeing in synthesis" Archived 1 February 2016 at the Wayback Machine, Mittal et al., IJIIDS, 5(2), 119–142, 2011 Applications of natural language processing, including information retrieval (i.e. text mining) and machine translation: * Russell & Norvig 2003, pp. 840–857, * Luger & Stubblefield 2004, pp. 623–630 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. 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. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 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. 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Retrieved 26 April 2018. Domingos 2015. Artificial brain arguments: AI requires a simulation of the operation of the human brain * Russell & Norvig 2003, p. 957 * Crevier 1993, pp. 271 and 279 A few of the people who make some form of the argument: * Moravec 1988 * Kurzweil 2005, p. 262 * Hawkins & Blakeslee 2005 The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. Goertzel, Ben; Lian, Ruiting; Arel, Itamar; de Garis, Hugo; Chen, Shuo (December 2010). "A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures". Neurocomputing. 74 (1–3): 30–49. doi:10.1016/j.neucom.2010.08.012. Nilsson 1983, p. 10. Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. Retrieved 20 February 2020. CNN 2006. Using AI to predict flight delays Archived 20 November 2018 at the Wayback Machine, Ishti.org. N. Aletras; D. Tsarapatsanis; D. Preotiuc-Pietro; V. Lampos (2016). "Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective". PeerJ Computer Science. 2: e93. doi:10.7717/peerj-cs.93. "The Economist Explains: Why firms are piling into artificial intelligence". The Economist. 31 March 2016. Archived from the original on 8 May 2016. Retrieved 19 May 2016. Lohr, Steve (28 February 2016). "The Promise of Artificial Intelligence Unfolds in Small Steps". The New York Times. Archived from the original on 29 February 2016. Retrieved 29 February 2016. Frangoul, Anmar (14 June 2019). "A Californian business is using A.I. to change the way we think about energy storage". CNBC. Archived from the original on 25 July 2020. Retrieved 5 November 2019. Wakefield, Jane (15 June 2016). "Social media 'outstrips TV' as news source for young people". BBC News. Archived from the original on 24 June 2016. Smith, Mark (22 July 2016). "So you think you chose to read this article?". BBC News. Archived from the original on 25 July 2016. Brown, Eileen. "Half of Americans do not believe deepfake news could target them online". ZDNet. Archived from the original on 6 November 2019. Retrieved 3 December 2019. The Turing test: Turing's original publication: * Turing 1950 Historical influence and philosophical implications: * Haugeland 1985, pp. 6–9 * Crevier 1993, p. 24 * McCorduck 2004, pp. 70–71 * Russell & Norvig 2003, pp. 2–3 and 948 Dartmouth proposal: * McCarthy et al. 1955 (the original proposal) * Crevier 1993, p. 49 (historical significance) The physical symbol systems hypothesis: * Newell & Simon 1976, p. 116 * McCorduck 2004, p. 153 * Russell & Norvig 2003, p. 18 Dreyfus 1992, p. 156. Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". The Mathematical Objection: * Russell & Norvig 2003, p. 949 * McCorduck 2004, pp. 448–449 Making the Mathematical Objection: * Lucas 1961 * Penrose 1989 Refuting Mathematical Objection: * Turing 1950 under "(2) The Mathematical Objection" * Hofstadter 1979 Background: * Gödel 1931, Church 1936, Kleene 1935, Turing 1937 Graham Oppy (20 January 2015). "Gödel's Incompleteness Theorems". Stanford Encyclopedia of Philosophy. Archived from the original on 22 April 2016. Retrieved 27 April 2016. These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail. Stuart J. Russell; Peter Norvig (2010). "26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection". Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4. even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations. Mark Colyvan. An introduction to the philosophy of mathematics. Cambridge University Press, 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail." Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society". Contemporary Social Science: 1–15. doi:10.1080/21582041.2018.1563803. ISSN 2158-2041. "Ethical AI Learns Human Rights Framework". Voice of America. Archived from the original on 11 November 2019. Retrieved 10 November 2019. Crevier 1993, pp. 132–144. In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool.[216] Joseph Weizenbaum's critique of AI: * Weizenbaum 1976 * Crevier 1993, pp. 132–144 * McCorduck 2004, pp. 356–373 * Russell & Norvig 2003, p. 961 Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Wendell Wallach (2010). Moral Machines, Oxford University Press. Wallach, pp 37–54. Wallach, pp 55–73. Wallach, Introduction chapter. Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. "Machine Ethics". aaai.org. Archived from the original on 29 November 2014. Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis. 1: 88–100. Archived from the original on 11 June 2012. Brooks, Rodney (10 November 2014). "artificial intelligence is a tool, not a threat". Archived from the original on 12 November 2014. "Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence". Observer. 19 August 2015. Archived from the original on 30 October 2015. Retrieved 30 October 2015. Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. Archived from the original on 8 March 2005. Retrieved 11 October 2018. See also this link Archived 8 April 2011 at the Wayback Machine Horst, Steven, (2005) "The Computational Theory of Mind" Archived 11 September 2018 at the Wayback Machine in The Stanford Encyclopedia of Philosophy Searle 1980, p. 1. This version is from Searle (1999), and is also quoted in Dennett 1991, p. 435. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." [230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently

Please Read Me First. This is a set of java file of my final version of electronic artifacts. This is a game to map my experience in Disney World, in Orlando during this spring break. However, because of my limited skills in computer science, I really have no idea how to simplify the process to run the game. Sorry for the inconvenience. In order to run the game, you may need to install JAVA. I hope the following links will help you.     http://www.oracle.com/technetwork/java/javase/downloads/index-jsp-138363.html#javasejdk
    http://www.cc.gatech.edu/~simpkins/teaching/gatech/cs1331/guides/install-java.html My main file is called Disney. You can call Disney in console to start the game. However, I failed to putting all the things inside Disney file. Therefore, you may also need to call AdventureLand, MainStreet, and FrontierLand to start other three games. I hope this will help you. Sorry again for the inconvenience. 1. the structure of my project My project only focused on my trip in Magic Kingdom, one part of Disney world in Orlando. It is a game which guides players to choose from six sub-games, which match six sections of the park, Main Street U.S.A, Tomorrowland, Adventureland, Frontierland, Fantasyland and Liberty Square. I chose one of the rides I took in each section which, from my perspective, shows what I found interesting in Disney world. I changed what I experienced in the park into a small computer game. I want to share my experience with others while they play my games. In the following part of self reflection, I explain the background, rules and other things about each game. For convenience of matching them, I use different color to mark different parts. I hope it will help readers a little bit when they are lost in my disordered reflections. 1. the hall of presidents - Liberty Square 2. Festivall parade - Fantasyland (I explain this one in the part of technology skill limitations) 3. Big Thunder Mountain Railroad - Frontierland 4.talking with Woody- Adventureland 5. Stitch Store - Tomorrowland 6. lunch time - Main Street USA 3. my reflection of the trip in Disney World from dream to reality When I exited Disney resort, I found a sign along the street welcomed people back to real world. Actually, when I was in Orlando, I couldn't believe as an adult, people can mess up fantasy world in the theme parks and the real world. Nevertheless, I felt I was still in fantasy world, when I dreamed twice that I fought for the key to open the door of future. As is known to all, while sleeping, people always dream about what people thinks in the daytime. Therefore, my dream shows that my mind still stayed in the world with Mickey and Donald. I believe that it is experiencing fantasy world which is the source of the greatest happiness people get from theme park. On the one hand, everybody has pressure in real life especially for adults. They can get out of pressure for a day trip in theme park. They can experience different lives here with cartoon characters. On the other hand, sometimes, it is a really hard task to fulfill some dreams, such as being a princess. However, in Disney world, you can dress up the same as Snow White, waiting for your prince; you can go to space by rocket; you can also travel all over the world in one day and enjoy the food of each country. These are all the magic of theme parks. Therefore, in my game, I learnt the way which Disney design their rides to focus on the background story of the game instead of the game itself. For example, there is a ride called Big Thunder Mountain Railroad, which streaks through a haunted gold-mining town aboard a rollicking runaway mine train. The views around the ride were like a gold mining town. There were tools for gold-mining around the railroad and the railroad looked like very old. In order to show riders that it was a haunted gold-mining town, the train always took a sudden turn or speed up quickly to scare people. I decided to name one of my game, which was inspired by this ride, the same name, Big Thunder Mountain Railroad. Instead of sitting inside the mine train to travel around the haunted town, mine was for users to use keyboard to control the train to travel around the gridding railroad. I place traps inside several parts of gridding to "scare" players, who cannot know where traps are until they get into them. If I know how to use animation, I will show scary pictures when players drive their train to the traps. Unlike the ride in Disney, my players can no longer travel once they encounter a trap because their train may have some problems to keep moving. Also, the main goal in the game is to find the gold. However, as we know, finding gold is really hard. Therefore, players must go to find Aladdin's Wonderful lamp where also places inside the gridding while players cannot see its exact place until they happen to drive inside the part where lamp is. Aladdin's Wonderful lamp will show players the map of the gold and when people get to the gold mine, they win. However, there is another limitation of the game. Haunted town is so dangerous during the night. Therefore, players only have 12 hours to finish the task. Train can drive one square in 20 min. Therefore, train can only move 36 times or they will also be caught by traps. In this game, I want to show audiences I have a background story like rides in Disney World. Players need to find the gold in a haunted gold-mining town. Also, in order to show the relationship with Disney, I use Aladdin's Wonderful Lamp as the guide for the players, which is a well known characters in Disney cartoon. I created another game, called talking with Woody to show the magic power of Disney characters. There are a lot of chances to meet Disney characters in Disney world. On the one hand, travelers, especially small kids, are really excited to meet the characters they watched on TV. I think some kids may believe they take pictures with real Mickey Mouse. On the hand, staffs in Disney who wear the costumes are really tired. It was hot in Orlando last week, but all costumes were very heavy. I was moved by the staffs inside Mickey. They also need to mimic the actions of characters and also need to show kindness and warmness to children. It seems like a really hard job. Therefore, I decide to show this part of Disney in my project as well. I decided to use Woody, a toy all the toys look up to. He is smart, kind and brave like a cowboy should be. He is more than a top, he is friend to everyone enjoying the movie Toy. In order to create an interactive game, I planned to ask players to guide Woody. Players need to call Woody before their instructions. For instance, if players say (actually players are typing) "Woody, please sit down", Woody will sit down (actually, there will be another line on the screen showing the same as players import). However, if players are rude and just say "sit down" without calling Woody, Woody won't act (actually there is just nothing showing up on the screen). great facilities to provide convenience to everyone The facilities to satisfy needs for special groups of people, like small kids or disabled people, are well developed. In the past in China, it seemed impossible for parents to take infants and small kids to travel. The road is not flat or wide enough for strollers or wheelchairs. However, in Disney world, everything seemed like well prepared for everyone to use. There are strollers rentals, and electric conveyance vehicles rentals, which are available to rent throughout Disney world. There are baby care center for mothers to feed, change and nurse little ones. There are locker rentals for storing personal items. There are also hearing disability services which have sign language interpretation to help disabled people to enjoy fantasy world. There are still a lot other convenient services in Disney world. I think the purpose of these services show the pursue of equality among everyone in the world. On the one hand, I am really touched by the availability of these services here. It seems Disney try its best to service everyone who have desire to experience fantasy land. On the other hand, in this way, Disney can attract more travelers in order to make more money in some ways. Also, in Disney, it seems like a tradition that there are stores at the exit of the famous rides. Somebody may think it is just a strategy to make people shopping a lot. However, I think it also provides some convenience that travelers can buy souvenirs where is memorable. For example, when I finished my trip in Escape Stitch, I entered a store with a lot of kinds of Stitch, like Stitch pillow, Stitch key chain and so on. I really want to buy something in order to remind me the wonderful feelings. Therefore, I showed my opinion inside my game as well. I wrote one part is for shopping. The items are different kinds of Stitch. My codes can act as a robot to help customers to shop in the store. There are a lot of restaurants in Disney. Maps of Disney are full of restaurants' name. The greatest things about the food are in Epcot, I experienced different counties in one day. I felt like I was in fast travel in different parts of the world and tasted their special food and snacks while I was on the way. I remembered I was still eating Japanese food when I was in "Mexico". It was a great experience. However, there were always a long waiting lines for the all restaurants. People needed to reserve a table a day before their trip and even they had the reservation, they still needed to wait for a long time. I think Disney may need some good ways to fix the problems of waiting for a long time. I have no idea of changing the situation of restaurants, but I think if there are robots to customers to order in fast food restaurant, it may help a lot. Thus, I have another code to customers to order in Plaza Restaurant. If this kind of robots can work in the real life, people can order by themselves and there will be more staffs available to prepare food. theme park uses interesting ways to teach knowledge of boring topics Theme part is also a great source of learning knowledge, especially for kids. They use Disney characters, interesting shows, or even games to teach useful things. The ways change the boring knowledge to interesting things, which always attract children's attention. The most amazing one was an interactive game in Epcot's Innoventions, called "where's the fire?", which teaches adults and children basic fire safety in a fun and entertaining way. About every five minutes, the players waiting in line are divided into two groups and move into the home's entry. Here, a host will explain the object of the game and lay out the rules. The scenario is this: you are on a mission to discover a number of fire hazards commonly found around the house. To do this, you move from room to room, looking for potential risks. To help in the task, each player is given a special "safely light" to help uncover lurking dangers. The rooms are large projection screens. When a hazard is discovered, all persons in the room must shine their safety light on the same spot. when they do, the hazard is rendered harmless and points are assigned. After playing in the game to find the hazardous things in the house, I learned a lot of safety tips. It is much easier to remember the tips I learned during the game than those I learned on textbook or internet. I believe kids will enjoy the games and learn from them as well. I also tried to show this reflection in my project. Thus, I planned to make a game, called the hall of presidents, which test people's knowledge of presidents in USA. However, I failed to achieve the goal of making it an entertaining game instead of a quiz. My game was still like a quiz. However, because it is the only code which can work well inside my big game. I decide to still hold the game for my projects in order to what my original ideas are. 4. technology skill limitations I feel terribly sorry for my limited skills in CS. It is my first time to learn JAVA this semester. I just begin to learn the core concepts of JAVA this month. When I choose to use java code for this project, I know I will face plentiful limitations and problems. Here I want to express my gratitude to Dr. Johnson, who encouraged me not to give up my ideas. To be honest, I have no idea of how to change a java code into a real game with animations. I know the background story of the game is more important for English course and pictures are the best way to show the background, but I have no idea to show all these things by JAVA coding. Therefore, I choose to use videos for my presentation. In this way, I can show my animation inside the videos while the code clue of my game is still composed by JAVA coding. Also, video gives me a lot of freedom when choose my contents for presentation. I can explain a lot details of my project clearly through videos. For example, I found the festival parade in the magic kingdom was great and I wanted to share the experience in my project by showing the pictures or videos. However, because of the technology limitations, I can only show the videos in my presentations. Also, I mistakenly deleted my videos which I shot on my trip Orlando, I can only share others' parade show...... Also, I want to apologize for the incompleteness of my game. I only dedicated to writing codes for Magic Kingdom, a part of my trip during spring break. Writing codes is a really time consuming task for me. In general, I need to spend more than eight hours to finish one project for my CS assignment this semester. While for this project, the final artifacts are composed of several parts of codes and in the end I need to write the father code in order to take care of my code family for spring break. Due to my limitation in writing codes, I can only finish one part of Disney world. However, I think my code shows all my reflections and perspectives during my trip, even though it looks like it only shows one part of my trip. The terrible mistake I made is that I found out the most of my codes I wrote had significant errors on Tuesday. I went to CS TA office for help, while the errors were still impossible to fix in order to achieve the goal I planned to get. Consequently, my game have to be separated into several parts. Instead of a big game having others as sub-games inside the big one, my final artifacts are composed by several small games. I need to start them one by one. It may cause some inconvenience for players to map their trip in Disney world.

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href="tag_figcaption.asp">&lt;figcaption&gt;</a> <a target="_top" href="tag_figure.asp">&lt;figure&gt;</a> <a target="_top" href="tag_font.asp">&lt;font&gt;</a> <a target="_top" href="tag_footer.asp">&lt;footer&gt;</a> <a target="_top" href="tag_form.asp">&lt;form&gt;</a> <a target="_top" href="tag_frame.asp">&lt;frame&gt;</a> <a target="_top" href="tag_frameset.asp">&lt;frameset&gt;</a> <a target="_top" href="tag_hn.asp">&lt;h1&gt; - &lt;h6&gt;</a> <a target="_top" href="tag_head.asp">&lt;head&gt;</a> <a target="_top" href="tag_header.asp">&lt;header&gt;</a> <a target="_top" href="tag_hr.asp">&lt;hr&gt;</a> <a target="_top" href="tag_html.asp">&lt;html&gt;</a> <a target="_top" href="tag_i.asp">&lt;i&gt;</a> <a target="_top" href="tag_iframe.asp">&lt;iframe&gt;</a> <a target="_top" href="tag_img.asp">&lt;img&gt;</a> <a target="_top" href="tag_input.asp">&lt;input&gt;</a> <a target="_top" href="tag_ins.asp">&lt;ins&gt;</a> <a target="_top" href="tag_kbd.asp">&lt;kbd&gt;</a> <a target="_top" href="tag_label.asp">&lt;label&gt;</a> <a target="_top" href="tag_legend.asp">&lt;legend&gt;</a> <a target="_top" href="tag_li.asp">&lt;li&gt;</a> <a target="_top" href="tag_link.asp">&lt;link&gt;</a> <a target="_top" href="tag_main.asp">&lt;main&gt;</a> <a target="_top" href="tag_map.asp">&lt;map&gt;</a> <a target="_top" href="tag_mark.asp">&lt;mark&gt;</a> <a target="_top" href="tag_meta.asp">&lt;meta&gt;</a> <a target="_top" href="tag_meter.asp">&lt;meter&gt;</a> <a target="_top" href="tag_nav.asp">&lt;nav&gt;</a> <a target="_top" href="tag_noframes.asp">&lt;noframes&gt;</a> <a target="_top" href="tag_noscript.asp">&lt;noscript&gt;</a> <a target="_top" href="tag_object.asp">&lt;object&gt;</a> <a target="_top" href="tag_ol.asp">&lt;ol&gt;</a> <a target="_top" href="tag_optgroup.asp">&lt;optgroup&gt;</a> <a target="_top" href="tag_option.asp">&lt;option&gt;</a> <a target="_top" href="tag_output.asp">&lt;output&gt;</a> <a target="_top" href="tag_p.asp" class="active">&lt;p&gt;</a> <a target="_top" href="tag_param.asp">&lt;param&gt;</a> <a target="_top" href="tag_picture.asp">&lt;picture&gt;</a> <a target="_top" href="tag_pre.asp">&lt;pre&gt;</a> <a target="_top" href="tag_progress.asp">&lt;progress&gt;</a> <a target="_top" href="tag_q.asp">&lt;q&gt;</a> <a target="_top" href="tag_rp.asp">&lt;rp&gt;</a> <a target="_top" href="tag_rt.asp">&lt;rt&gt;</a> <a target="_top" href="tag_ruby.asp">&lt;ruby&gt;</a> <a target="_top" href="tag_s.asp">&lt;s&gt;</a> <a target="_top" href="tag_samp.asp">&lt;samp&gt;</a> <a target="_top" href="tag_script.asp">&lt;script&gt;</a> <a target="_top" href="tag_section.asp">&lt;section&gt;</a> <a target="_top" href="tag_select.asp">&lt;select&gt;</a> <a target="_top" href="tag_small.asp">&lt;small&gt;</a> <a target="_top" href="tag_source.asp">&lt;source&gt;</a> <a target="_top" href="tag_span.asp">&lt;span&gt;</a> <a target="_top" href="tag_strike.asp">&lt;strike&gt;</a> <a target="_top" href="tag_strong.asp">&lt;strong&gt;</a> <a target="_top" href="tag_style.asp">&lt;style&gt;</a> <a target="_top" href="tag_sub.asp">&lt;sub&gt;</a> <a target="_top" href="tag_summary.asp">&lt;summary&gt;</a> <a target="_top" href="tag_sup.asp">&lt;sup&gt;</a> <a target="_top" href="tag_svg.asp">&lt;svg&gt;</a> <a target="_top" href="tag_table.asp">&lt;table&gt;</a> <a target="_top" href="tag_tbody.asp">&lt;tbody&gt;</a> <a target="_top" href="tag_td.asp">&lt;td&gt;</a> <a target="_top" href="tag_template.asp">&lt;template&gt;</a> <a target="_top" href="tag_textarea.asp">&lt;textarea&gt;</a> <a target="_top" href="tag_tfoot.asp">&lt;tfoot&gt;</a> <a target="_top" href="tag_th.asp">&lt;th&gt;</a> <a target="_top" href="tag_thead.asp">&lt;thead&gt;</a> <a target="_top" href="tag_time.asp">&lt;time&gt;</a> <a target="_top" href="tag_title.asp">&lt;title&gt;</a> <a target="_top" href="tag_tr.asp">&lt;tr&gt;</a> <a target="_top" href="tag_track.asp">&lt;track&gt;</a> <a target="_top" href="tag_tt.asp">&lt;tt&gt;</a> <a target="_top" href="tag_u.asp">&lt;u&gt;</a> <a target="_top" href="tag_ul.asp">&lt;ul&gt;</a> <a target="_top" href="tag_var.asp">&lt;var&gt;</a> <a target="_top" href="tag_video.asp">&lt;video&gt;</a> <a target="_top" href="tag_wbr.asp">&lt;wbr&gt;</a> </div> <br><br> </div> </div> </div> <div class="w3-main w3-light-grey" id="belowtopnav" style="margin-left: 220px; padding-top: 44px;"> <div class="w3-row w3-white"> <div class="w3-col l10 m12" id="main"> <div id="mainLeaderboard" style="overflow:hidden;"> <!-- MainLeaderboard--> <!--<pre>main_leaderboard, all: [728,90][970,90][320,50][468,60]</pre>--> <div id="adngin-main_leaderboard-0" data-google-query-id="CJPA_sueqvcCFXiOSwUd2fYBLg"><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1__container__" style="border: 0pt none;"><iframe id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//main_leaderboard_1" 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</div> <br> <div class="w3-example"> <h3>Example</h3> <p>A paragraph is marked up as follows:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs1" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <p>More "Try it Yourself" examples below.</p> <hr> <h2>Definition and Usage</h2> <p>The <code class="w3-codespan">&lt;p&gt;</code> tag defines a paragraph.</p> <p>Browsers automatically add a single blank line before and after each <code class="w3-codespan">&lt;p&gt;</code> element.</p> <p><strong>Tip:</strong> Use CSS to <a href="/html/html_css.asp">style paragraphs</a>.</p> <hr> <h2>Browser Support</h2> <table class="browserref notranslate"> <tbody><tr> <th style="width:20%;font-size:16px;text-align:left;">Element</th> <th style="width:16%;" class="bsChrome" title="Chrome"></th> <th style="width:16%;" class="bsEdge" title="Internet Explorer / Edge"></th> <th style="width:16%;" class="bsFirefox" title="Firefox"></th> <th style="width:16%;" class="bsSafari" title="Safari"></th> <th style="width:16%;" class="bsOpera" title="Opera"></th> </tr><tr> <td style="text-align:left;">&lt;p&gt;</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> <td>Yes</td> </tr> </tbody></table> <hr> <h2>Global Attributes</h2> <p>The <code class="w3-codespan">&lt;p&gt;</code> tag also supports the <a href="ref_standardattributes.asp">Global Attributes in HTML</a>.</p> <hr> <h2>Event Attributes</h2> <p>The <code class="w3-codespan">&lt;p&gt;</code> tag also supports the <a href="ref_eventattributes.asp">Event Attributes in HTML</a>.</p> <hr> <div id="midcontentadcontainer" style="overflow:auto;text-align:center"> <!-- MidContent --> <!-- <p class="adtext">Advertisement</p> --> <div id="adngin-mid_content-0" data-google-query-id="CKfs_8ueqvcCFXiOSwUd2fYBLg"><div id="sn_ad_label_adngin-mid_content-0" class="sn_ad_label" style="color:#000000;font-size:12px;margin:0;text-align:center;">ADVERTISEMENT</div><div id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1__container__" style="border: 0pt none; display: inline-block; width: 300px; height: 250px;"><iframe frameborder="0" src="https://56d0da6c34aaa471db22bb4266aac656.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html" id="google_ads_iframe_/22152718,16833175/sws-hb//w3schools.com//mid_content_1" title="3rd party ad content" name="" scrolling="no" marginwidth="0" marginheight="0" width="300" height="250" data-is-safeframe="true" sandbox="allow-forms allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-top-navigation-by-user-activation" role="region" aria-label="Advertisement" tabindex="0" data-google-container-id="8" style="border: 0px; vertical-align: bottom;" data-load-complete="true"></iframe></div></div> </div> <hr> <h2>More Examples</h2> <div class="w3-example"> <h3>Example</h3> <p>Align text in a paragraph (with CSS):</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="attributecolor" style="color:red"> style<span class="attributevaluecolor" style="color:mediumblue">="text-align:right"</span></span><span class="tagcolor" style="color:mediumblue">&gt;</span></span>This is some text in a paragraph.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_align_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Style paragraphs with CSS:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>html<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>head<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>style<span class="tagcolor" style="color:mediumblue">&gt;</span></span><span class="cssselectorcolor" style="color:brown"><br>p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br>&nbsp; color<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> navy<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; text-indent<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 30px<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; text-transform<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> uppercase<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span><br></span><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/style<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/head<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>body<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span>Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.<span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/body<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/html<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_p_style_css" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>&nbsp;More on paragraphs:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br>This paragraph<br>contains a lot of lines<br>in the source code,<br> but the browser <br>ignores it.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_paragraphs2" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <div class="w3-example"> <h3>Example</h3> <p>Poem problems in HTML:</p> <div class="w3-code notranslate htmlHigh"> <span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>p<span class="tagcolor" style="color:mediumblue">&gt;</span></span><br>My Bonnie lies over the ocean.<br>My Bonnie lies over the sea.<br>My Bonnie lies over the ocean.<br>Oh, bring back my Bonnie to me.<br><span class="tagnamecolor" style="color:brown"><span class="tagcolor" style="color:mediumblue">&lt;</span>/p<span class="tagcolor" style="color:mediumblue">&gt;</span></span> </div> <a target="_blank" href="tryit.asp?filename=tryhtml_poem" class="w3-btn w3-margin-bottom">Try it Yourself »</a> </div> <hr> <h2>Related Pages</h2> <p>HTML tutorial: <a href="/html/html_paragraphs.asp">HTML Paragraphs</a></p> <p>HTML DOM reference: <a href="/jsref/dom_obj_paragraph.asp">Paragraph Object</a></p> <hr> <h2>Default CSS Settings</h2> <p>Most browsers will display the <code class="w3-codespan">&lt;p&gt;</code> element with the following default values:</p> <div class="w3-example"> <h3>Example</h3> <div class="w3-code notranslate cssHigh"><span class="cssselectorcolor" style="color:brown"> p <span class="cssdelimitercolor" style="color:black">{</span><span class="csspropertycolor" style="color:red"><br>&nbsp; display<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> block<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; margin-top<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; margin-bottom<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 1em<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp; margin-left<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br>&nbsp;&nbsp;margin-right<span class="csspropertyvaluecolor" style="color:mediumblue"><span class="cssdelimitercolor" style="color:black">:</span> 0<span class="cssdelimitercolor" style="color:black">;</span></span><br></span><span class="cssdelimitercolor" style="color:black">}</span> 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CS self-learning

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My self-paced Java + DSA learning repo as a CS student — covering core Java, OOP, generics, collections, multithreading, and DSA concepts including arrays, linked lists, trees, graphs, sorting, and dynamic programming. Includes handwritten implementations, LeetCode solutions, mini projects, and notes. Built to learn deeply, not just to pass exams.

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计算机科学与技术自学指南

Communication Skills Have you ever attempted to convey the desired information? Maybe you took a stab at pitching an earth shattering venture, you were tasked with running a departmental meeting, or you expected to convey a convincing presentation. You did as well as can possibly be expected, however all you got were vague looks and ungainly quiets. Odds are, there wasn't much amiss with what you were attempting to say; it's simply that you expected to chip away at how you said it. The best communicators rouse and spur individuals, help them to make successful move, and expel obstructions to their comprehension. Lamentably, numerous businessmen think that its difficult to impart viably — we've all gotten tremendous, language ridden messages, and we've encountered the misconception, dissatisfaction and missed open doors that accompany them. Be that as it may, with the right aptitudes, anybody can turn into an outstanding communicator Add to My Personal Learning Plan. 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C# Nakov Book and Teleric Demos and excercises

Some records of learning Berkeley CS61A-23-SP

notes in BUPT

I learn HTML and CSS myself.This project is my related code.

My journey of self-learning CS in summer 2025.

I am an undergraduate CS student at Dalian University of Technology. My research interests focus on Vision-Language-Action (VLA) and robot manipulation, with an emphasis on robustness and self-supervised learning under distribution shifts.

我的cs自学之路

혼자 공부하는 컴퓨터 구조 및 운영체제 스터디

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