Advertisement

Natural Computing

, Volume 14, Issue 4, pp 603–635 | Cite as

Text comprehension and the computational mind-agencies

  • Romi BanerjeeEmail author
  • Sankar K. Pal
Article
First Online:
  • 343 Downloads

Abstract

Guided by a polymath approach—encompassing neuroscience, philosophy, psychology and computer science, this article describes a novel ‘cognitive’ computational mind framework for text comprehension in terms of Minsky’s ‘Society of Mind’ and ‘Emotion Machine’ theories. Observing a top-down design method, we enumerate here the macrocosmic elements of the model—the ‘agencies’ and memory constructs, followed by an elucidation on the working principles and synthesis concerns. Besides corroboration of results of a dry-run test by thoughts generated by random human subjects; the completeness of the conceptualized framework has been validated as a consequence of its total representation of ‘text understanding’ functions of the human brain, types of human memory and emulation of the layers of the mind. A brief conceptual comparison, between the architecture and existing ‘conscious’ agents, has been included as well. The framework, though observed here in its capacity as a text comprehender, is capable of understanding in general. A cognitive model of text comprehension, besides contributing to the ‘thinking machines’ research enterprise, is envisioned to be strategic in the design of intelligent plagiarism checkers, literature genre-cataloguers, differential diagnosis systems, and educational aids for children with reading disorders. Turing’s landmark 1950 article on computational intelligence is the principal motivator behind our research initiative.

Keywords

Society of mind Thinking machines Reflective cognitive architecture Concept-granulation Natural computation Artificial general intelligence 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This project is being carried out under the guidance of Professor Sankar K. Pal who is an INAE Chair Professor and J.C. Bose Fellow of the Government of India. The authors acknowledge Alan Turing as the prime inspiration for the work described herein.

References

  1. Ariely D (2008) Predictably irrational: the hidden forces that shape our decisions. Harper Collins, NYGoogle Scholar
  2. Ashby WR (1952) Design for a brain. Butler and Tanner Ltd., LondonGoogle Scholar
  3. Baars BJ (1988) A cognitive theory of consciousness. Cambridge University Press, CambridgeGoogle Scholar
  4. Baars BJ (1997) In the theater of consciousness: the workspace of the mind. Oxford University Press, OxfordCrossRefGoogle Scholar
  5. Baars BJ (2002) The conscious access hypothesis: origins and recent evidence. Trends Cogn Sci 6(1):47–52CrossRefGoogle Scholar
  6. Backus J (1978) Can programming be liberated from the von Neumann style? A functional style and its algebra of programs (ACM Turing Award lecture). Commun ACM 21(8):613–641zbMATHMathSciNetCrossRefGoogle Scholar
  7. Baddeley AD (1966) The influence of acoustic and semantic similarity on long-term memory for word sequences. Quart J Exp Psychol 18(4):302–309CrossRefGoogle Scholar
  8. Banaji MR, Greenwald AG (2013) Blindspot: hidden biases of good people. Delacorte Press, NYGoogle Scholar
  9. Banerjee R, Pal SK (2013) The Z-number enigma: a study through an experiment. In: Yager RR, Abbasov AM, Reformat MR, Shahbazova SN (eds) Soft computing: state of the art theory and novel applications, vol. 291 of studies in fuzziness and soft computing, Springer, Berlin/Heidelberg, pp 71–88Google Scholar
  10. Baum EB (2009) Project to build programs that understand. In: Goertzel B, Hitzler P, Hutter M (eds) In: Proceedings of second conference on artificial general intelligence, vol. 8 of advances in intelligent systems research, Atlantis Press, Paris, pp 1–6Google Scholar
  11. Bobrow DG (1964) Natural language input for a computer problem solving system. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  12. Brains in Silicon. http://www.stanford.edu/group/brainsinsilicon/index.html. Accessed 8 April 2014
  13. Bush V (1945) As we may think. Atl Mon 176(1):101–108Google Scholar
  14. Charniak E (1972) Toward a model of children’s story comprehension. Technical report, MIT Artificial Intelligence LaboratoryGoogle Scholar
  15. Chomsky N (1959) A review of B.F. Skinner’s “verbal behavior”. Language 35(1):26–58CrossRefGoogle Scholar
  16. Chomsky N (1991) Linguistics and cognitive science: problems and mysteries. In: The chomskyan turn, Blackwell Publishing, Oxford, pp 26–53Google Scholar
  17. Chugani HT, Behen ME, Muzik O, Juhász C, Nagy F, Chugani DC (2001) Local brain functional activity following early deprivation: a study of post institutionalized Romanian orphans. NeuroImage 14(6):1290–1301CrossRefGoogle Scholar
  18. Clark HH (1997) Dogmas of understanding. Discourse Process 23:567–598CrossRefGoogle Scholar
  19. Conway MA, Pleydell-Pearce CW (2000) The construction of autobiographical memories in the self-memory system. Psychol Rev 107(2):261–288CrossRefGoogle Scholar
  20. Cristobal G, Schelkens P, Thienpont H (eds) (2011) Optical and digital image processing: fundamentals and applications. Wiley-VCH Verlag GmbH and Co., KGaA, WeinheimGoogle Scholar
  21. Dennett DC (2013) The normal well-tempered mind. http://www.edge.org/conversation/the-normal-well-tempered-mind
  22. Eccles JC, Ito M, Szentagothai J (1967) The cerebellum as a neuronal machine. Springer-Verlag, NYCrossRefGoogle Scholar
  23. Erman LD, Hayes-Roth F, Lesser VR, Reddy DR (1980) The Hearsay-II speech-understanding system: integrating knowledge to resolve uncertainty. ACM Comput Surv 12(2):213–253CrossRefGoogle Scholar
  24. Ferrucci D, Brown E, Chu-Carroll J, Fan J, Gondek D, Kalyanpur AA, Lally A, Murdock JW, Nyberg E, Prager J, Schlaefer N, Welty C (2010) Building Watson: an overview of the DeepQA project. AI Mag 31(3):59–78Google Scholar
  25. Franklin S (2003) IDA: a conscious artifact? J Conscious Stud 10:47–66MathSciNetGoogle Scholar
  26. Franklin S, Patterson FG (2006) The LIDA architecture: adding new modes of learning to an intelligent, autonomous, software agent. Integrated Design and Process Technology, San DiegoGoogle Scholar
  27. Gladwell M (2005) Blink: the power of thinking without thinking. Little Brown and Company (Hachette Book Group), NYGoogle Scholar
  28. Gottlieb J, Oudeyer P, Lopes M, Baranes A (2013) Information-seeking, curiosity, and attention: computational and neural mechanisms. Trends Cogn Sci 17(11):585–593CrossRefGoogle Scholar
  29. Grosz BJ (2012) What question would Turing pose today? AI Mag 33(4):73–81Google Scholar
  30. Harley TA (2008) The Psychology of Language: From Data to Theory, 3rd edn. Psychology Press—Taylor and Francis Group, New YorkGoogle Scholar
  31. Harrison H, Minsky M (1992) Unpublished chapters of “The Turing Option”. http://web.media.mit.edu/~minsky/papers/option.chapters.txt
  32. Havasi C, Speer R, Alonso J (2007) Conceptnet 3: a flexible, multilingual semantic network for common sense knowledge. In: Proceedings of recent advances in natural language processing, pp 27–29Google Scholar
  33. Hayes-Roth B (1985) A blackboard architecture for control. Artif Intell 26:251–321CrossRefGoogle Scholar
  34. Hewitt C (1970) Planner: a language for manipulating models and proving theorems in a robot, Massachusetts Institute of Technology—Project MAC—Artificial Intelligence—Memo 168, August 1970Google Scholar
  35. Hikosaka O, Takikawa Y, Kawagoe R (2000) Role of the basal ganglia in the control of purposive saccadic eye movements. Physiol Rev 80(3):953–978Google Scholar
  36. Hunt J (2002) Blackboard architectures. Technical Report 1, JayDee Technology Ltd., WiilshireGoogle Scholar
  37. Husserl E (1970) Logical investigations (Translated from German). Routledge and Kegan Paul Ltd, LondonGoogle Scholar
  38. Jankowski A, Skowron A, Swiniarski RW (2013) Interactive complex granules. In: Szczuka MS, Czaja L, Kacprzak M (eds) CS & P, vol. 1032 of CEUR workshop proceedings, vol 1032. pp 206–218. CEUR-WS.org
  39. Kahneman D (2011) Thinking, fast and slow. Farrar, Straus and Giroux, NYGoogle Scholar
  40. Kofka K (1935) Principles of gestalt psychology. Lund Humphries, LondonGoogle Scholar
  41. Kokinov BN (1994) The dual cognitive architecture: a hybrid multi-agent approach. In: Conn A (ed) Proceedings of 11th european conference on artificial intelligence (ECAI), John Wiley and Sons, Ltd, pp 203–207Google Scholar
  42. Kokinov B (1989) About modelling some aspects of human memory. In: Man-computer interaction research (MACINTER-II), Elsevier, Amsterdam, pp 349–359Google Scholar
  43. Kowalski R (2011) Computational logic and human thinking: how to be artificially intelligent. Cambridge University Press, NYCrossRefGoogle Scholar
  44. Langley P, Laird JE, Rogers S (2009) Cognitive architectures: research issues and challenges. Cogn Syst Res 10(2):141–160CrossRefGoogle Scholar
  45. Li L, Chen G, Yang S (2013) Construction of cognitive maps to improve e-book reading and navigation. Comput Educ 60(1):32–39CrossRefGoogle Scholar
  46. Lieberman H, Liu H, Singh P, Barry B (2004) Beating common sense into interactive applications. AI Mag 25(4):63–76Google Scholar
  47. Lin TY (1997) Granular computing. Technical report, Announcement of the BISC special interest group on granular computingGoogle Scholar
  48. Liu H (2004) Montylingua: an end-to-end natural language processor with common sense. web.media.mit.edu/~hugo/montylingua
  49. Loewenstein G (1994) The psychology of curiosity: a review and reinterpretation. Psychol Bull 116(1):75–98CrossRefGoogle Scholar
  50. Maes P (1987) Concepts and experiments in computational reflection. In: Meyrowitz NK (ed) Proceedings of conference on object-oriented programming systems, languages and applications (OOPSLA), ACM, NY, pp 147–155Google Scholar
  51. Majumdar A, Sowa J, Stewart J (2008) Pursuing the goal of language understanding. In: Eklund P, Haemmerlé O (eds) Proceedings of 16th international conference on conceptual structures: knowledge visualization and reasoning, Springer-Verlag, Berlin, pp 21–42Google Scholar
  52. McCarthy J (2008) The well-designed child. Artif Intell 172(18):2003–2014zbMATHCrossRefGoogle Scholar
  53. McCarthy J (1995) Making robots conscious of their mental states. In: Machine intelligence, Oxford University Press, NY, pp 3–17Google Scholar
  54. McCarthy J (1959) Programs with commonsense. In: Semantic information processing, MIT Press, MA, pp 403–418Google Scholar
  55. McCauley L, Franklin S, Bogner M (2000) An emotion-based “conscious” software agent architecture. In: Paiva A (ed) Affective interactions, vol. 1814 of lecture notes on artificial intelligence, Springer-Verlag, Berlin, pp 107–120Google Scholar
  56. McGaugh JL (2004) The amygdala modulates the consolidation of memories of emotionally arousing experiences. Annu Rev Neurosci 27:1–28CrossRefGoogle Scholar
  57. Mead C (1990) Neuromorphic electronic systems. Proc IEEE 78:1629–1636CrossRefGoogle Scholar
  58. Miller GA (1955) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 101(2):343–352CrossRefGoogle Scholar
  59. Minsky ML (1986) The society of mind. Simon and Schuster Inc, NYGoogle Scholar
  60. Minsky ML (1992) Future of AI technology. Toshiba Rev 47(7):139Google Scholar
  61. Minsky M (2000) Commonsense based interfaces. Commun ACM 43(8):67–73CrossRefGoogle Scholar
  62. Minsky ML (2006) The emotion machine: commonsense thinking, artificial intelligence, and the future of the human mind. Simon and Schuster Inc, NYGoogle Scholar
  63. Minsky M (1975) A framework for representing knowledge. In: The psychology of computer vision, McGraw-Hill, NY, pp 211–277Google Scholar
  64. Modha DS, Ananthanarayanan R, Esser SK, Ndirango A, Sherbondy AJ, Singh R (2011) Cognitive computing. Commun ACM 54(8):62–71CrossRefGoogle Scholar
  65. Morgan B (2013) A substrate for accountable layered systems. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  66. Morgan B (2010) Funk2: a distributed processing language for reflective tracing of a large critic-selector cognitive architecture. In: proc. fourth IEEE international conference on self-adaptive and self-organizing systems workshop (SASOW), IEEE Computer Society, CA, pp 269–274Google Scholar
  67. von Neumann J (2012) The computer and the brain, 3rd edn. Yale University Press, New Haven and LondonGoogle Scholar
  68. Pal SK, Banerjee R (2013) Context-granulation and subjective information quantification. Theor Comput Sci 448:2–14MathSciNetCrossRefGoogle Scholar
  69. Pal SK, Banerjee R, Dutta S, Sen Sarma S (2013) An insight into the Z-number approach to CWW. Fundam Inform 124(1–2):197–229Google Scholar
  70. Payne SJ, Reader WR (2006) Constructing structure maps of multiple on-line texts. Int J Hum Comput Stud 64(5):461–474CrossRefGoogle Scholar
  71. Picard R (1997) Affective computing. MIT Press, MACrossRefGoogle Scholar
  72. Pinker S (1997) How the mind works. W. W. Norton & Company, NYGoogle Scholar
  73. Pinker S (2007) The stuff of thought: language as a window into human nature. Penguin Books (Viking Press), NY, USAGoogle Scholar
  74. Price CJ (2000) The anatomy of language: contributions from functional neuroimaging. J Anat 197:335–359CrossRefGoogle Scholar
  75. Ramachandran VS, Blakeslee S (1999) Phantoms in the brain: probing the mysteries of the human mind. William Morrow and Company (Harper Collins), New YorkGoogle Scholar
  76. Ramachandran VS, Hubbard EM (2001) Neural cross wiring and synesthesia. J Vis 1(3):67CrossRefGoogle Scholar
  77. Ramachandran VS, Hubbard EM (2003) The phenomenology of synaesthesia. J Conscious Stud 10(8):49–57Google Scholar
  78. Robinson K, Aronica L (2013) Finding your element: how to discover your talents and passions and transform your life. Viking (Penguin Group), NYGoogle Scholar
  79. Roese NJ (1997) Counterfactual thinking. Psychol Bull 121(1):133–148CrossRefGoogle Scholar
  80. Rothkopf EZ (1971) Incidental memory for location of information in text. J Verbal Learn Verbal Behav 10(6):608–613CrossRefGoogle Scholar
  81. Rugg MD, Yonelinas AP (2003) Human recognition memory: a cognitive neuroscience perspective. Trends Cogn Sci 7(7):313–319CrossRefGoogle Scholar
  82. Ryle G (1949) The concept of mind. University of Chicago Press, USAGoogle Scholar
  83. Seth AK (2010) The grand challenge of consciousness (opinion article). Front Psychol 1(5):1–2zbMATHGoogle Scholar
  84. Seth AK, Izhikevich E, Reeke GN, Edelman GM (2006) Theories and measures of consciousness: an extended framework. Proc Natl Acad Sci (PNAS) 103(28):10799–10804CrossRefGoogle Scholar
  85. Singh P (2003b) Examining the society of mind. Comput Inform 22(6):521–543zbMATHMathSciNetGoogle Scholar
  86. Singh P, Barry B, Liu H (2004a) Teaching machines about everyday life. BT Technol J 22(4):227–240CrossRefGoogle Scholar
  87. Singh P, Minsky ML (2004) An architecture for cognitive diversity. In: Davis D (ed) Visions of mind. Idea Group Inc., LondonGoogle Scholar
  88. Singh P, Minsky M, Eslick I (2004b) Computing commonsense. BT Technol J 22(4):201–210CrossRefGoogle Scholar
  89. Singh P (2003) A preliminary collection of reflective critics for layered agent architectures. In: Proceedings of the safe agents workshop (AAMAS), Melbourne, AustraliaGoogle Scholar
  90. Singh P (2005) EM-ONE: an architecture for reflective commonsense thinking. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  91. Singh P, Minsky ML (2003) An architecture for combining ways to think. In: Proceedings of the international conference of the integration of knowledge intensive multi-agent systems, pp 669–674Google Scholar
  92. Sloman A (1978) The computer revolution in philosophy: philosophy, science and models of mind. The Harvester Press Ltd., SussexGoogle Scholar
  93. Sloman A (1984) Towards a computational theory of mind. In: Artificial intelligence—human effects, Ellis Horwood, UK, pp 173–182Google Scholar
  94. Sloman A (2001) Varieties of affect and the CogAff architecture schema. In: Proceedings symposium on emotion, cognition, and affective computing AISB’01 convention, pp 39–48Google Scholar
  95. Snaider J, McCall R, Franklin S (2011) The LIDA framework as a general tool for AGI. In: Schmidhuber J, Thórisson KR, Looks M (eds) Proceedings of 4th international conference in artificial general intelligence, vol 6830 of lecture notes in computer science, Springer, pp 133–142Google Scholar
  96. Stallman RM, Sussman GJ (1977) Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artif Intell 9:135–196zbMATHCrossRefGoogle Scholar
  97. Stocco A, Lebiere C, Anderson JR (2010) Conditional routing of information to the cortex: a model of the basal ganglia’s role in cognitive coordination. Psychol Rev 117(2):541–574CrossRefGoogle Scholar
  98. Sussman GJ (1973) A computational model of skill acquisition. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  100. Todorovic D (2008) Gestalt principles. Scholarpedia 3(12):5345CrossRefGoogle Scholar
  101. Turing AM (1950) Computing machinery and intelligence. Mind 49:433–460MathSciNetCrossRefGoogle Scholar
  102. Turing A (1949) Intelligent machinery. http://www.alanturing.net/intelligent_machinery/
  103. Wertheimer M (1923) Laws of organization in perceptual forms. Psycologische Forsch 4:301–350CrossRefGoogle Scholar
  104. Winograd E (1988) Some observations on prospective remembering. In: Practical aspects of memory: current research and issues, vol 1. John Wiley, NJ, pp 348–353Google Scholar
  105. Winograd T (1971) Procedures as a representation of data in a computer program for understanding natural language. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  106. Winston PH (1970) Learning structural descriptions from examples. PhD thesis, MITGoogle Scholar
  107. Wolf M (2007) Proust and the squid: the story and science of the reading brain. Harper Collins, NYGoogle Scholar
  108. Zadeh LA (1994) Fuzzy logic, neural networks and soft computing. Commun ACM 37(3):77–84MathSciNetCrossRefGoogle Scholar
  109. Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst 4(2):103–111MathSciNetCrossRefGoogle Scholar
  110. Zadeh LA (1998) Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Comput 2:23–25CrossRefGoogle Scholar
  111. Zadeh LA (2011) A note on Z-numbers. Inf Sci 181(14):2923–2932zbMATHMathSciNetCrossRefGoogle Scholar
  112. Zhang Z, Franklin S, Dasgupta D (1998) Metacognition in software agents using classifier systems. In: Mostow J, Rich C (eds) Proceedings of fifteenth national conference on artificial intelligence and tenth innovative applications of artificial intelligence conference, AAAI Press, CA, pp 83–88Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Center for Soft Computing ResearchIndian Statistical InstituteKolkataIndia

Personalised recommendations

Cite article

Buy options