Academia.eduAcademia.edu

The Mismatch between Human and Machine Knowledge

Profile image of maryse vlaanderenmaryse vlaanderen

1994

descriptionSee full PDF

Abstract

Thinking reed ... 346. Pensée fait la grandeur de l'homme. 347. L'homme n'est qu'un roseau, le plus faible de la nature; mais c'est un roseau pensant. Il ne faut pas que l'univers entier s'arme pour l'écraser: une vapeur, une goutte d'eau, suffit pour le tuer. Mais, quand l'univers l'écraserait, l'homme serait encore plus noble que ce qui le tue, parce qu'il sait qu'il meurt, et l'avantage que l'univers a sur lui; l'univers n'en sait rien. Toute notre dignité consiste donc en la pensée. Pensées. Blaise Pascal (1623-1662). 1 Silicon; Si, the 14th element in the periodic table of elements. The name silicon derives from the Latin silex or silicis, meaning "flint" or "hard stone". Silicon is one of the most common of all the elements, occurring in the Earth's crust as sand.... A(n)...property resulting from the electronic structure of silicon is that it functions as an intrinsic semiconductor. 2 The silicon chip controls our daily life. It replaces boring jobs and creates new ones. It is present in most of the devices that accompany us, that make our lives pleasant or watch over our safety. It is doing a lot of thinking that man used to do himself. After the Stone, the Bronze, and the Iron Age, we are now living in the Sand Age.

Related papers

Stone toolmaking and the evolution of human culture and cognition.pdf
Matteo Rivera

Although many species display behavioural traditions, human culture is unique in the complexity of its technological, symbolic and social contents. Is this extraordinary complexity a product of cognitive evolution, cultural evolution or some interaction of the two? Answering this question will require a much better understanding of patterns of increasing cultural diversity, complexity and rates of change in human evolution. Palaeolithic stone tools provide a relatively abundant and continuous record of such change, but a systematic method for describing the complexity and diversity of these early technologies has yet to be developed. Here, an initial attempt at such a system is presented. Results suggest that rates of Palaeolithic culture change may have been underestimated and that there is a direct relationship between increasing technological complexity and diversity. Cognitive evolution and the greater latitude for cultural variation afforded by increasingly complex technologies may play complementary roles in explaining this pattern.

View PDFchevron_right
SAND TO SILICON: The amazing story of digital technology -By SHIVANAND KANAVI
Shivanand Kanavi

2004

The book traces the origin and evolution of Digital Technology: Semiconductors, Microelectronics, Fibre Optics, Laser & Optical Networking, Telecom, Computing and the Internet. It was the first to chronicle seminal Indian contributions in the making of modern digital technology and very briefly how digital technology change India.

View PDFchevron_right
Recognizing Complex Cognition through Innovative Technology in Stone
Lyn Wadley

Cognitive complexity is defined here as the capacity for abstract thought, analogical reasoning, cognitive fluidity, innovative thought, complex goal-directed actions, flexibility in problemsolving, multi-tasking, task switching, response inhibition and planning over long distances or time. Some of these attributes are archaeologically recognizable in transformative technologies such as heat treatment of rocks and ochre, and the manufacture of compound adhesives and paints. Advanced executive functions of the brain are also required for remote capture during snaring, which is implied by circumstantial archaeological evidence. Some technologies seem good indicators of complex cognition and the emphasis here is on making the connection, but this does not mean that cognition necessarily drove innovation in the past any more than it does today. The recursive relationships between cognition, social behaviour and technology mean that change cannot be attributed to a single stimulus. Free email alerts Keep up-to-date with new material -sign up at http://journals.cambridge.org/alerts Price information is available at: http://journals.cambridge.org/caq Classical Quarterly is available online at:

View PDFchevron_right
Clay: The Entanglement of Earth in the Age of Clay
Susan D. Gillespie

The Impact of Materials on Society (open access textbook), 2021

This chapter showcases human engagements with the most primal material of all—earth itself—beginning in the Neolithic period (which began ca. 9000 BCE in the Fertile Crescent), when people relied on domesticated plants and animals for their livelihood. The Neolithic has also been called the Age of Clay because clay and soils were critical materials for many aspects of daily life. A case study of an important Neolithic settlement, Çatalhöyük, demonstrates how people and clay became interdependent on each other, resulting in an “entanglement” that influenced human actions and values. The Neolithic entanglement with clay, multiplied countless times all over the globe, led to significant historical changes in human society that still reverberate today. This case study also provides general insights for understanding the relationships between humans and materials. How people engage with the potential and actualized properties of materials in production processes is key to understanding the historical trajectories of the impacts of materials on societies.

View PDFchevron_right
Recognizing Complex Cognition through Innovative Technology in Stone Age and Palaeolithic Sites
Lyn Wadley

Cambridge Archaeological Journal, 2013

Cognitive complexity is defined here as the capacity for abstract thought, analogical reasoning, cognitive fluidity, innovative thought, complex goal-directed actions, flexibility in problem-solving, multi-tasking, task switching, response inhibition and planning over long distances or time. Some of these attributes are archaeologically recognizable in transformative technologies such as heat treatment of rocks and ochre, and the manufacture of compound adhesives and paints. Advanced executive functions of the brain are also required for remote capture during snaring, which is implied by circumstantial archaeological evidence. Some technologies seem good indicators of complex cognition and the emphasis here is on making the connection, but this does not mean that cognition necessarily drove innovation in the past any more than it does today. The recursive relationships between cognition, social behaviour and technology mean that change cannot be attributed to a single stimulus.

View PDFchevron_right

Related topics

  • Computer Science

    • Related papers

    On Typical Materials Acting as the Dividing Standard of the Development Stages of Human Substance Civilization
    B. W. Zhang

    2012

    During more than three million years, the substance civilization of human society went through the Stone Age, the Bronze Age, the Iron Age, the Steel and Cement Age, and the Silicon Age. At the beginning of the new century, the human society has entered into the Nanomaterials Age, which indicates that a completely new substance civilization of human society has started. This paper analyses and discusses why the typical materials serve as the only standard for dividing the eras of the substance civilization of human society. The author argues about the subject of dividing the substance civilization of human society. The goal is to initiate broad and thorough discussion of that subject so as to get a rather thorough understanding about it.

    View PDFchevron_right
    Experimental heat treatment of silcrete implies analogical reasoning in the Middle Stone Age
    Linda Prinsloo

    Journal of Human Evolution, 2014

    Siliceous rocks that were not heated to high temperatures during their geological formation display improved knapping qualities when they are subjected to controlled heating. Experimental heat treatment of South African silcrete, using open fires of the kind used during the Middle Stone Age, shows that the process needed careful management, notwithstanding recent arguments to the contrary. Silcrete blocks fractured when heated on the surface of open fires or on coal beds, but were heated without mishap when buried in sand below a fire. Three silcrete samples, a control, a block heated underground with maximum temperature between 400 and 500 C and a block heated in an open fire with maximum temperature between 700 and 800 C, were analysed with X-ray powder diffraction (XRD), X-ray fluorescence (XRF), optical microscopy, and both Fourier transform infrared (FTIR) and Raman spectroscopy.

    View PDFchevron_right
    Material Culture and Technological Determinism
    Hector MacIntyre

    2015

    This dissertation has two results. First, I argue that each of the two basic components of technological determinism (TD)-what I call the inexorability thesis and the autonomy thesis-are plausible claims on a naturalistic stance. Second, I argue that a normative model for the design of cognitive systems can guide the practice of cognitive engineering, e.g. the task of building cognitive aids and enhancements. TD conjoins two logically independent but empirically related claims. The inexorability thesis is the claim that technology change is an evolutionary process. I defend this claim against considerations raised by Lewens, most notably the lack of a robust account of artifact reproduction that would underwrite genuine transmission. I consider (but reject) the solution of memeticists to this problem. I find that theorists of cultural evolution, e.g. Boyd and Richerson (among others), do present a plausible response. Technologies can be said to evolve via the cumulative selective process of cultural retention.

    View PDFchevron_right
    Archaeology and Engineering: What can engineers learn from the material cultures of the past?
    Linda Westphalen

    The study of material cultures has a long history. Called 'archaeology' in academic contexts, the relationship between objects created by humans and their cultural lives is used by archaeologists to make inferences about how people live and lived, how their technology functions/ed in mechanical as well as in social, political and economic senses, and how innovation supported changes in all these domains. Materials and cultures are thus intertwined. All things created by humans that function in day to day existence have cultural, social, economic and political significances. Since engineers are at the forefront of technological innovation, an understanding of the cultural contexts of material objects is, if not essential, key to comprehending the contexts and impacts that innovations could have. In a sense, Engineering is archaeology of the future. Thus Engineering Educators are not only bound to explore the pedagogical (how we teach) and the curricular (what we teach), they ...

    View PDFchevron_right
    Technology and Material Life
    Kacy Hollenback, Michael Schiffer

    Oxford Handbook of Material Culture Studies, 2010

    Humans live in a world of things. They are surrounded by artefacts. Referred to as the social shaping of technology, this has been an interesting area of research in the recent past. The focus of this article is on the ‘material life’ of human beings, and the place of technology within it. The authors approach this topic from the discipline of archaeology, specifically behavioural archaeology, but also draw on research in other fields. This article, expands this framework to include the life histories of technologies and associated material practices. This article further contextualizes contemporary technology studies, primarily in archaeology, and considers how theoretical concepts from behavioural archaeology and social constructivist studies of technology might be combined. Archaeological studies of technology are explained in details in the following section with special emphasis on performance characteristics. This article also explains the life history of technology which helps us conceptualize material practices in relation to objects and technologies.

    View PDFchevron_right
    Squeezing minds from stones: Cognitive archaeology and the evolution of the human mind
    Karenleigh A Overmann

    Oxford University Press, 2019

    Cognitive archaeology is a relatively new interdisciplinary science that uses cognitive and psychological models to explain archeological artifacts like stone tools, figurines, and art. Squeezing Minds from Stones is a collection of essays from early pioneers in the field, like archaeologists Thomas Wynn and Iain Davidson, and evolutionary primatologist William McGrew, to 'up and coming' newcomers like Shelby Putt, Ceri Shipton, Mark Moore, James Cole, Natalie Uomini, and Lana Ruck. Their essays address a wide variety of cognitive archaeology topics, including the value of experimental archaeology, primate archaeology, the intent of ancient tool makers, and how they may have lived and thought.

    View PDFchevron_right
    Christopher Baber, Anthony Chemero, and Jamie Hall (2017). What the Jeweller’s Hand Tells the Jeweller’s Brain: Tool Use, Creativity and Embodied Cognition. Philosophy and Technology. https://doi.org/10.1007/s13347-017-0292-0
    Chris Baber, Tony Chemero

    The notion that human activity can be characterised in terms of dynamic systems is a well-established alternative to motor schema approaches. Key to a dynamic systems approach is the idea that a system seeks to achieve stable states in the face of perturbation. While such an approach can apply to physical activity, it can be challenging to accept that dynamic systems also describe cognitive activity. In this paper, we argue that creativity, which could be construed as a 'cognitive' activity par excellence, arises from the dynamic systems involved in jewellery making. Knowing whether an action has been completed to a 'good' standard is a significant issue in considering acts in creative disciplines. When making a piece of jewellery, there a several criteria which can define 'good'. These are not only the aesthetics of the finished piece but also the impact of earlier actions on subsequent ones. This suggests that the manner in which an action is coordinated is influenced by the criteria by which the product is judged. We see these criteria as indicating states for the system, e.g. in terms of a space of 'good' outcomes and a complementary space of 'bad' outcomes. The skill of the craftworker is to navigate this space of available states in such a way as to minimise risk, effort and other costs and maximise benefit and quality of the outcome. In terms of postphenomonology, this paper explores Ihde's human-technology relations and relates these to the concepts developed here.

    View PDFchevron_right
    Archaeotechnology: studying technology from prehistory to the Middle Ages [2014]
    Srpsko Arheološko Društvo, Tomislav Hršak, Julka Kuzmanovic-Cvetkovic, Thilo Rehren, Jasna Vukovic, Tatjana Mihailović, Ilinka Atanasova, Dragana Rajković, Selena Vitezović, Ioan Alexandru Bărbat, Gordana Jeremic

    2014

    View PDFchevron_right
    Function follows form? The role of analogies in discovering the Stone Age
    Liisa Kunnas

    Archaeology and Analogy: Papers from the Eighth Theoretical Seminar of the Baltic Archaeologists (BASE) held at the University of Helsinki and Tvärminne Zoological Station, Hanko, Finland, November 30th – December 2nd, 2017. Interarchaeologia 6, 2020

    In 1723, Antoine de Jussieu presented his research to the French Academy of Sciences. He demonstrated how stone tools used by Caribbean and Canadian native populations uncannily resembled European ‘thunderbolts’, as Stone Age stone artefacts were known. Since the latter had similar appearance as the former, their origin and function must also be similar. Expanding the analogy, it could be presumed that European people had once been culturally similar to the native peoples of America. Although the archaeological and chronological concept of the Stone Age did not form until later, it already existed as a philosophical concept in relative chronology. Ideas of stone being used as raw material before metals, and the division of the past into successive technological epochs were discussed in the Antiquity, as well as during the subsequent centuries. However, ethnographic and technological analogies were essential in connecting the philosophical theory with the material record of the past. The analogy between Stone Age and the contemporary native populations expanded from technology to culture, having a lasting impact on the way Stone Age is perceived.

    View PDFchevron_right
    Evolution of thought and its influence on society and technology © "An engineering vision"
    Jorge Cardenas

    Evolution of thought and its influence on society and technology, 2022

    If you're curious about what lies behind engineering, physics, science, and technology, I invite you to embark on a journey through fascinating and uncharted worlds within these pages. This book offers the chance to uncover new answers, while also challenging many of the assumptions we often take for granted. 307 pages of discovery await.

    View PDFchevron_right

    References (127)

    1. Summary To summarize this section, we can expand the conclusion of the previous section:
    2. There is a mismatch between the human and machine approach of knowledge,with
    3. Knowledge acquisition is not a part, but an integrated aspect of knowledge-base building.
    4. There is a discrepancy between knowledge acquisition and knowledge representation.
    5. How much of the knowledge is still "hidden" or "tacit"?
    6. The various levels of knowledge should be monitored. BIBLIOGRAPHY Arnheim 1969 (1974) Rudolf Arnheim. Visual thinking. (U. of California Press 1969, 1974)
    7. Bar-Hillel 1970
    8. Y. Bar-Hillel. Aspects of language. (The Magnes Press. Jerusalem 1970)
    9. Barr, Cohen, and Feigenbaum 1981
    10. Avron Barr and Edward A.Feigenbaum, eds. The Handbook of artificial intelligence. Volumes I and II. Paul R.Cohen and Edward A.Feigenbaum, eds. Volume III. (William Kaufmann 1981)
    11. Barr, Cohen, and Feigenbaum 1989
    12. Avron Barr, Paul R.Cohen, and Edward A.Feigenbaum, eds. The Handbook of artificial intelligence. Volume IV. (Addison-Wesley Publishing Company, Inc. 1989) Bechtel and Abrahamsen 1990
    13. William Bechtel and Adele A. Abrahamsen. Beyond the exclusively propositional era. Synthese 82 (1990) Berliner 1980
    14. Hans Berliner. Computer backgammon. Scientific American 242 (1980) Bobrow and Winograd 1977
    15. Daniel G.Bobrow and Terry Winograd. An overview of KRL, a knowledge representation language. Cognitive Science 1 (1977)
    16. Boden 1977
    17. Margaret A.Boden. Artificial intelligence and natural man. (Basic Books, Inc., New York 1977) Boden 1989 (1988)
    18. Margaret A.Boden. Computer models of mind. Computational approaches in theoretical psychology. (Cambridge U.P. 1988, 1989)
    19. Brachman 1977
    20. Ronald J.Brachman. What's in a concept: structural foundations for semantic networks. International Journal of Man-Machine Studies 9 (1977) Brachman 1979
    21. Ronald J.Brachman. On the epistemological status of semantic networks. In N.V.Findler. Associative networks; representation and use of knowledge by computers. (Academic Press NY 1979) Brachman and Levesque 1985
    22. Ronald J.Brachman and Hector J.Levesque, eds. Readings in knowledge representation. (Morgan Kaufmann Publ., Inc. 1985) Brachman and Schmolze 1985
    23. Ronald J.Brachman and James G.Schmolze. An overview of the KL-ONE knowledge representation system. Cognitive Science 9 (1985) Brand and Harnish 1986
    24. Myles Brand and Robert M.Harnish, eds. The representation of knowledge and belief. (University of Arizona Press 1986) Buchanan et al. 1983
    25. Bruce G.Buchanan, et al. Constructing an expert system. In: Hayes-Roth, Waterman, and Lenat 1983 Buchanan and Smith 1988
    26. Bruce G.Buchanan and Reid G.Smith. Fundamentals of experts systems. Annual Review of Computer Science 3 (1988) Cassirer 1975 (1955) Ernst Cassirer. The philosophy of symbolic forms. 3 Vls. (Yale University Press 1955, 1975) Charniak 1981
    27. Eugene Charniak. A common representation for problem-solving and language-comprehension information. Artificial Intelligence 16 (1981). Charniak and McDermott 1985
    28. Eugene Charniak and Drew McDermott. Introduction to artificial intelligence. (Addison-Wesley Publ. Comp. Inc. 1985) Clancey 1984
    29. William J.Clancey, Knowledge acquisition for classification expert systems. Heuristic Programming Project; Working Paper (HPP 84-18)
    30. William J.Clancey. The knowledge level reinterpreted: modeling how systems interact. Machine Learning 4 (1989) Clancey 1991
    31. William J.Clancey. The frame of reference problem in the design of intelligent machines. In: Kurt Vanlehn, ed. Architectures for intelligence. (LEA, Hillsdale NJ, 1991)
    32. Clark 1989
    33. Andy Clark. Microcognition: Philosophy, cognitive science, and parallel distributed processing. (The MIT Press 1989) Compton and Jansen 1990
    34. P.Compton and R. Jansen. A philosophical basis for knowledge acquisition. Knowledge Acquisition 2 (1990) Cooke 1992
    35. Nancy J.Cooke. Modeling human expertise in expert systems. In: Hoffman 1992
    36. Daniel C.Dennett. The logical geography of computational approaches: a view from the East Pole. In: Brand and Harnish 1986. Doorman 1989
    37. S.J.Doorman. Methodologische opmerkingen over een gedachten-experiment. Kennis en Methode 13 (1989) Dreyfus 1979
    38. H.L.Dreyfus. What computers can't do. (New York 1972, rev. ed. 1979) Edwards 1972 (1967)
    39. Paul Edwards, ed. The Encyclopedia of philosophy. (Collier and Macmillan, Inc. New York 1967, 1972) Feigenbaum 1977
    40. E.A.Feigenbaum. The art of artificial intelligence. In: Proceedings of the 5th IJCAI. 1977 Feigenbaum and Feldman 1963
    41. E.A.Feigenbaum and J.Feldman. Computers and Thought. (McGraw Hill 1963) Fetzer 1988
    42. James H.Fetzer, ed. Aspects of Artificial Intelligence. (Kluwer, Dordrecht 1988)
    43. Fodor 1975
    44. Jerry A.Fodor. The language of thought. (The Harvester Press 1975, 1976) Fodor en Pylyshyn 1988
    45. Jerry A.Fodor and Zenon W.Pylyshyn. Connectionism and cognitive architecture: a critical analysis. Cognition 28 (1988) Frege 1969 (1892)
    46. Gottlob Frege. Ueber Sinn und Bedeutung. Zeitschrift für Philosophie und philosophische Kritik NF 100 (1892). Also in: Funktion, Begriff, Bedeutung. Fünf logische Studien. (Vandenhoeck & Ruprecht, Göttingen 1969) Gentner and Stevens 1983
    47. D.Gentner and A.L.Stevens, eds. Mental Models. (Erlbaum 1983) Haugeland 1981
    48. John Haugeland, ed. Mind design. (MIT Press 1981, 1982)
    49. Hayes 1979 (1985)
    50. Patrick J.Hayes. The logic of frames. In: Brachman and Levesque 1985. Orig. 1979
    51. Hayes-Roth, Waterman, and Lenat 1983
    52. Frederick Hayes-Roth, Donald A.Waterman, and Douglas B.Lenat. Building expert systems. (Addison-Wesley Publishing Comp. 1983) Hoffman 1992
    53. Robert R.Hoffman, ed. The psychology of expertise; cognitive research and empirical AI. (Springer 1992) Holland et al 1986 (1989)
    54. J.H.Holland, K.G.Holyoak, R.E.Nisbett, and P.R.Thagard. Induction, processes of inference, learning, and discovery. (MIT Press, 1986, 1989) Israel and Brachman 1986
    55. David J.Israel and Ronald J.Brachman. Some remarks on the semantics of representation language. In: M.L.Brodie, John Mylopoulos and Joachim W.Schmidt, eds. On conceptual modeling. (Springer 1986) Johnson-Laird 1980
    56. P.N.Johnson-Laird. Mental models in cognitive science. Cognitive Science 4 (1980) Johnson-Laird 1983 (1987)
    57. P.N. Johnson-Laird. Mental models. Towards a cognitive science of language, inference, and consciousness. (CUP 1983, 1987)
    58. Johnson-Laird 1989a
    59. P.N.Johnson-Laird. Human experts and expert systems. In: Murray and Richardson 1989
    60. Johnson-Laird 1989b
    61. P.N.Johnson-Laird. Mental models. In: Posner 1989
    62. Johnson-Laird 1991
    63. P.N.Johnson-Laird. Deduction (Erlbaum 1991)
    64. Johnson-Laird and Watson 1977 (1985)
    65. P.N.Johnson-Laird and P.C.Watson, eds. Thinking; Readings in cognitive science. (CUP 1977, 1985) Kitcher 1990
    66. Patricia Kitcher. Kant's dedicated cognitivist system. In: J-C. Smith, ed. Historical foundations of cognitive science. (Kluwer Academic Publ. Dordrecht 1990) Lenat and Feigenbaum 1991
    67. D.B.Lenat and E.A.Feigenbaum. On the thresholds of knowledge. Artificial Intelligence 47 (1991) Levesque and Brachman 1985
    68. Hector J.Levesque and Ronald J.Brachman. A fundamental trade-off in knowledge representation and reasoning. In: Brachman and Levesque 1985
    69. David Littman. Constructing expert systems as building mental models; or Toward a cognitive ontology for expert systems. In: K.Morik, ed. Knowledge representation in machine learning (Springer 1989) Miller 1981
    70. George A.Miller. Trends and debates in cognitive psychology. Cognition 10 (1981) Minsky 1968
    71. M.Minsky, ed. Semantic information processing. (MIT Press 1968) Minsky 1975 (1985)
    72. Marvin Minsky. A framework for representing knowledge. In: Brachman and Levesque 1985. Orig. in P.H.Winston ed. The psychology of computer vision (NY 1975). Also in Haugeland 1981 Murray and Richardson 1989
    73. Linda A.Murray and John T.E. Richardson, eds. Intelligent systems in a human context. (OUP 1989) Neisser 1976
    74. Ulrich Neisser. Cognition and reality. (W.H.Freeman and Comp. San Francisco 1976) Newell 1982
    75. Allen Newell. The knowledge level. Artificial Intelligence 18 (1982) Newell 1993
    76. Allen Newell. Reflections on the knowledge level. Artificial Intelligence 59 (1993) Newell and Simon 1963
    77. A. Newell and H.A.Simon. GPS, a program that simulates human thought. In: Feigenbaum and Feldman 1963 Newell and Simon 1981 (1975)
    78. A.Newell and H.A.Simon. Computer science as empirical inquiry: symbols and search. In: Haugeland 1981. Also in Communications of the Association for Computing Machinery 19 (1975) Norman 1980
    79. Donald A.Norman. Twelve issues for cognitive science. Cognitive Science 4 (1980) Norman 1981
    80. Donald A.Norman. Perspectives on cognitive science. (Ablex Publ. Corp. Norwood, NJ. 1981) Norman 1983
    81. Donald A.Norman. Some observations on mental models. In: Gentner and Stevens 1983 Normore 1990
    82. Calvin G.Normore. Ockham on mental language. In: J-C. Smith, ed. Historical foundations of cognitive science. (Kluwer Academic Publ. Dordrecht 1990) Ogden and Richards 1972 (1923)
    83. C.K.Ogden and I.A.Richards. The meaning of meaning. A study of the influence of language upon thought and of the science of symbolism. (Routledge & Kegan Paul Ltd. London 1972)
    84. Osherson & Smith 1990
    85. Daniel N.Osherson and Edward E.Smith. An invitation to cognitive science. 3 Vls. (The MIT Press 1990) Polanyi 1967
    86. M.Polanyi. The tacit dimension. (Doubleday. New York 1967) Posner 1989
    87. M.I.Posner, ed. Foundations of cognitive science. (MIT Press 1989) Pylyshyn 1978
    88. Zenon W.Pylyshyn. Imagery and artificial intelligence. In: Savage 1978
    89. Quillian 1968
    90. M.R.Quillian. Semantic memory. In: Minsky 1968
    91. W.J. Rapaport. The Korean Room. In: Fetzer 1988. Regoczei and Hirst 1992
    92. Stephen B.Regoczei and Graeme Hirst. Knowledge and knowledge acquisition in the computational context. In: Hoffman 1992 Regoczei and Plantinga 1987
    93. Stephen Regoczei, Edwin P.O.Plantinga. Creating the domain of discourse: ontology and inventory. International Journal of Man-Machine Studies 27 (1987) Reichgelt 1991
    94. Han Reichgelt. Knowledge representation; an AI perspective. (Ablex, Norwood, NJ 1991) Rich 1983
    95. Elaine Rich. Artificial intelligence. (McGraw-Hill International Book Company 1983, 1991)
    96. Lance J.Rips. Mental muddles. In: Brand and Harnish 1986
    97. Rorty 1979
    98. Richard Rorty. Philosophy and the mirror of nature. (Princeton U.P. 1979)
    99. Rosch 1978
    100. Eleanor Rosch. Principles of categorization. In: E.Rosch and B.B.Lloyd, eds., Cognition and categorization. (Hillsdale 1978) Savage 1978
    101. C.W.Savage, ed. Perception & Cognition. Issues in the foundations of psychology. Minnesota Studies in the Philosophy of Science 9 (1978) Schank 1980
    102. Roger C.Schank. Language and memory. Cognitive Science 4 (1980) Schank and Abelson 1977
    103. R.C. Schank and R.P.Abelson. Scripts, plans, goals and understanding (Hillsdale NJ 1977) Schumacher and Czerwinski 1992
    104. Robert M.Schumacher and Mary P. Czerwinski. Mental models and the acquisition of expert knowledge. In: Hoffman 1992
    105. Searle 1980
    106. John R.Searle. Minds, brains, and programs. The Behavioral and Brain Sciences 3 (1980). Also in: Haugeland 1981 Shortliffe 1976
    107. E.H.Shortliffe. Computer-based medical consultations: MYCIN. (American Elsevier, New York 1976)
    108. Simon 1978 H.A.Simon. On the forms of mental representation. In: Savage 1978
    109. Simon 1989 H.A.Simon. Models of thought II. (Yale Univ. Press, New Haven 1989)
    110. Slatter 1987
    111. P.E.Slatter. Building expert systems: cognitive emulation. (Ellis Horwood 1987) Sloman 1971
    112. Aaron Sloman. Interactions between philosophy and artificial intelligence: the role of intuition and non-logical reasoning in intelligence. Artificial Intelligence 2 (1971) Sloman 1983
    113. Aaron Sloman. Under what conditions can a machine attribute meaning to symbols. In: Proceedings of the IJCAI 1983 Sloman 1985
    114. Aaron Sloman. What enables a machine to understand? In: Proceedings of the IJCAI 1985
    115. Brian C. Smith. Prologue to "Reflection and semantics in a procedural language". PhD diss. (Cambridge MA 1982). Also in Brachman and Levesque 1985
    116. Edward E.Smith. Concepts and induction. In: Posner 1989
    117. Edward E.Smith. Categorization. in: Osherson and Smith 1990.
    118. Sowa 1984 J.F.Sowa. Conceptual structures; Information processing in mind and machine. (Addison-Wesley 1984) Sowa 1992
    119. John F.Sowa. Conceptual analysis as a basis for knowledge acquisition. In: Hoffman 1992
    120. Visser 1985
    121. H.Visser. Kennistheoretische problemen van kunstmatige intelligentie. Tilburg University 1985 Vlaanderen 1990
    122. Marie José Vlaanderen. Automated knowledge acquisition for expert systems; an overview. Report Faculty of Philosophy. Erasmus University Rotterdam 1990
    123. Way 1991 Eileen Cornell Way. Knowledge representation and metaphor. (Kluwer 1991) Weizenbaum 1966
    124. Joseph Weizenbaum. ELIZA -A computer program for the study of natural language communication between man and machine. Communications of the ACM 6 (1966) Weizenbaum 1976
    125. Joseph Weizenbaum. Computer power and human reason. From judgement to calculation. (Freeman and Co 1976)
    126. Wilks 1977
    127. Y.A.Wilks. Methodological questions about artificial intelligence: Approaches to understanding natural language. Journal of Pragmatics 1 (1977)
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved