Skip to main content
SpringerLink
Log in

Epistemological Approach to the Process of Practice

  • Published:
Minds and Machines Aims and scope Submit manuscript

Abstract

Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence of a model for the process of practise in current approaches is a significant contributor to brittleness. This paper will investigate how the process of practise relates to deeper forms of contextual representations of knowledge. While researchers and developers of knowledge based systems have often incorporated the notion of context they treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts. This paper argues that the omission of these higher forms of context is one of the fundamental problems in the application and interpretation of symbolic knowledge. Finally, these ideas for modelling context will lead to the reinterpretation of situation cognition which makes a significant step towards a philosophy of knowledge that could lead to the modelling of the process of practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. G stands for Gegenstande in German.

  2. M stands for Merkmale in German.

  3. Merkwelt is the term used by Jakob von Uexkull in his 1934 paper ‘A Stroll through the Worlds of Animals and Men: A Picture Book of Invisible Worlds’, to refer to the complete set of environmental factors that have an affect on a species regardless of whether they are perceptible or not.

References

  • Adam, A. (2000). Deleting the subject: A feminist reading of epistemology in artificial intelligence. Minds and Machines, 10, 231–253.

    Article  Google Scholar 

  • Agre, P. E. (1990). Book review of plans and situated: Actions: The problem of human-machine communication. Artificial Intelligence, 43(3), 369–384.

    Article  Google Scholar 

  • Anderson, J. (1990). Cognitive psychology and its implications (3rd ed.). New York: W. H. Freeman and Company.

    Google Scholar 

  • Arbib, M. (1993). Book review—Allen Newell, unified theories of cognition. Artificial Intelligence, 59I have reprint in contemplating minds, 265.

  • Bachant, J., & McDermott, J. (1984). R1 revisited: Four years in the trenches. AI Magazine, 5(3), 21–32.

    Google Scholar 

  • Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. London: Cambridge University Press.

    Google Scholar 

  • Beydoun, G. (2000). Incremental knowledge acquisition for search control heuristics. Sydney: University of New South Wales.

    Google Scholar 

  • Birkhoff, G. (1938). Lattices and their applications. Bulletin of the American Mathematics Society, 44, 793–800.

    Article  MathSciNet  Google Scholar 

  • Boose, J. H., Bradshaw, J. M., Kitto, C. M., & Shema, D. B. (1989). From ETS to aquinas: Six years of knowledge acquisition tools. In Proceedings of the Fourth AAAI-sponsored Knowledge Acquisition for Knowledge-based Systems Workshop, Banff (pp. 5.1–5.17).

  • Bransford, J. D., McCarrell, N. S., Franks, J. J., & Nitsch, K. E. (1977). Toward unexplaining memory. In R. Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing: Toward an ecological psychology (pp. 431–466). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

    Google Scholar 

  • Brezillon, P. (1999). Context in artificial intelligence: II. Key elements of contexts. Computer and Artificial Intelligence, 18(5), 425–446.

    MATH  Google Scholar 

  • Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–159.

    Article  Google Scholar 

  • Clancey, W. J. (1991). Book review—Israel Rosenfield, the invention of memory: A new view of the brain. Artificial Intelligence, 50(2), 241–284.

    Article  Google Scholar 

  • Clancey, W. J., Sachs, P., Sierhuis, M., & van Hoof, R. (1998). BRAHMS: Simulating practice for work system design. International Journal of Human-Computer Studies, 49, 831.

    Article  Google Scholar 

  • Compton, P. (1992). Insight and knowledge. In AAAI spring symposium: Cognitive aspect of knowledge acquisition (pp. 57–63). Stanford: AAAI Press.

  • Compton, P., Horn, R., Quinlan, R., & Lazarus, L. (1989). Maintaining an expert system. In J. R. Quinlan (Ed.), Applications of expert systems (pp. 366–385). London: Addison Wesley.

    Google Scholar 

  • Compton, P., & Jansen, R. (1990). A philosophical basis for knowledge acquisition. Knowledge Acquisition, 2, 241–257.

    Article  Google Scholar 

  • Compton, P., & Jansen, R. (1988). Knowledge in context: A strategy for expert system maintenance. In Proceedings of Second Australian Joint Artificial Intelligence Conference (AI88), Sydney (Vol. 1, pp. 292–306).

  • Compton, P., & Kang, B. H. (1993). Knowledge histories. In Proceedings of 2nd Australian Cognitive Science Conference (pp. 139–141). Melbourne, Australia.

  • Compton, P., Kang, B. H., Preston, P., & Mulholland, M. (1993). Knowledge acquisition without analysis. In G. Boy & B. Gaines (Eds.), Knowledge acquisition for knowledge based systems (Vol. 723, pp. 278–299). Berlin: Springer.

    Google Scholar 

  • Compton, P., et al. (1988). Maintaining an expert system. In Proceedings of Fourth Australian Conference on Applications of Expert Systems, Sydney (pp. 110–129).

  • Compton, P., et al. (1991). Ripple down rules: Possibilities and limitations. In Proceedings of Sixth Banff Knowledge Acquisition for Knowledge-based Systems Workshop (KAW91), Banff (Vol. 1, pp. 6.1–6.18).

  • Dazeley, R. (2007). To the knowledge frontier and beyond: A hybrid system for incremental contextual-learning and prudence analysis. Ph.D. dissertation, University of Tasmania, 281 p.

  • Diaz-Agudo, B., & Gonzalez-Calero, P. A. (2001). Classification based retrieval using formal concept analysis. ICCBR 2001. Lecture Notes in Computer Science , 2080, 173–188.

  • Feigenbaum, E. A. (1977). The art of artificial intelligence: Themes and case studies in knowledge engineering. In International joint conference on artificial intelligence (pp. 1014–1029). Stanford: Stanford University.

  • Gaines, B. (2000). Knowledge science and technology: Operationalizing the enlightenment. In Proceedings of the 6th Pacific Knowledge Acquisition Workshop (pp. 97–124). Sydney: University of New South Wales.

  • Gaines, B., & Shaw, M. L. G. (1993). Knowledge acquisition tools based on personal construct psychology. The Knowledge Engineering Review, 8(1), 49–85.

    Article  Google Scholar 

  • Gennari, J. H., Musen, M. A., Fergerson, R. W., Grosso, W. E., Crubezy, M., Eriksson, H., et al. (2002). The evolution of protégé: An environment for Knowledge-Based Systems Development (Technical Report No. SMI-2002-0943): Standford Medical Informatics (SMI).

  • Guha, R. V., & Lenat, D. B. (1994). Enabling agents to work together. Communications of the ACM, 37(7), 127–142.

    Article  Google Scholar 

  • Jansen, R., & Compton, P. (1989). The knowledge dictionary: Storing different knowledge representations. In Proceedings of the 5th Australian Conference on Applications of Expert Systems (pp. 143–162). Sydney: CSIRO Division of Information Technology.

  • Jenkins, J. J. (1974). Remember that old theory of memory? Well, forget it! The American Psychologist, 29(11), 785–795.

    Article  Google Scholar 

  • Kang, B. H., & Compton, P. (1992). Knowledge acquisition in context: The multiple classification problem. In Proceedings of the 2nd Pacific Rim International Conference on Artificial Intelligence. Seoul, Korea.

  • Kang, B. H., & Compton, P. (1994). Multiple classification ripple down rules. In Third Japanese knowledge acquisition for knowledge-based systems workshop.

  • Kang, B. H., Compton, P., & Preston, P. (1995). Multiple classification ripple down rules: Evaluation and possibilities. In Proceedings of the 9th Knowledge Acquisition for Knowledge based Systems Workshop. Banff: University of Calgary.

  • Kelly, G. A. (1955). The psychology of personal constructs. New York: Norton.

    Google Scholar 

  • Kelly, G. A. (1970). A brief introduction to personal construct theory. In D. Bannister (Ed.), Perspectives in personal construct theory (pp. 1–29). London: Academic Press.

    Google Scholar 

  • Killin, J. (1993). Managing and maintaining an operational KADS system. In G. Schreiber, B. J. Wielinga & J. Breuker (Eds.), A principled approach to knowledge-based development (p. 457). London: Academic Press.

    Google Scholar 

  • Leake, D. B. (1996). Case-based reasoning: Experiences, lessons, and future directions. Menlo Park, CA: AAAI Press/MIT Press.

    Google Scholar 

  • Lenat, D. B. (1995). CYC: A large-scale investment in knowledge infrastructure. Communications of the ACM, 38(11), 33–38.

    Article  Google Scholar 

  • Lenat, D. B., & Feigenbaum, E. A. (1988). On the threshold of knowledge. In Proceedings of the Fourth Australian Conference on Applications of Expert Systems (pp. 31–56).

  • Lenat, D. B., & Feigenbaum, E. A. (1991). On the threshold of knowledge. Artificial Intelligence, 47(1), 185–250.

    Article  MathSciNet  Google Scholar 

  • Lenat, D. B., & Guha, R. V. (1990). Building large knowledge-based systems: Representation and inference in the cyc project. Reading, MA: Addison-Wesley Publishing Company, Inc.

    Google Scholar 

  • Lenat, D. B., et al. (1990). Cyc: Toward programs with common sense. Communications of the ACM, 33(8), 30–49.

    Article  Google Scholar 

  • Maes, P. (1990). Designing autonomous agents. Robotics and Autonomous Systems, 6(1), 1–196.

    Article  MathSciNet  Google Scholar 

  • Menzies, T. (1996). Assessing responses to situated cognition. In Proceedings of the 10th Banff Knowledge Acquisition for Knowledge-based Systems Workshop. Spain: Catelonia.

  • Menzies, T. (1998). Towards situated knowledge acquisition. International Journal of Human-Computer Studies, 49, 867–893.

    Article  Google Scholar 

  • Menzies, T., & Debenham, J. (2000). Expert system maintenance. In A. Kent & J. G. Williams (Eds.), Encyclopaedia of computer science and technology (pp. 35–54). New York, NY: Marcell Dekker Inc.

    Google Scholar 

  • Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113–126.

    Article  MathSciNet  Google Scholar 

  • Patel, V., & Ramoni, M. (1997). Cognitive models of directional inference in expert medical reasoning. In P. F. Feltovich & R. Hoffman (Eds.), Expertise in context (pp. 67–99). Cambridge, MA: MIT Press.

    Google Scholar 

  • Piaget, J. (1970). Genetic epistemology. New York, NY: Columbia University Press.

    Google Scholar 

  • Pittman, K., & Lenat, D. B. (1993). Representing knowledge in CYC–9, MCC Technical Report CYC-175-93P.

  • Popper, K. R. (1963). Conjectures and refutations. London: Routledge and Kegan Paul Ltd.

    Google Scholar 

  • Popper, K. R. (1968). Epistemology without a knowing subject. In B. V. Rootselaar (Ed.), Logic, methodology and philosophy of science (Vol. III (pp. 333–373). Amsterdam: North-Holland.

    Google Scholar 

  • Preston, P., Edwards, G., & Compton, P. (1993). A 1600 rule expert system without knowledge engineers. In Moving Towards Expert Systems Globally in the 21st Century (pp. 220–228). (Proceedings of the Second World Congress on Expert Systems 1993)

  • Preston, P., Edwards, G., & Compton, P. (1994). A 2000 rule expert system without a knowledge engineer. In Proceedings of the 8th AAAI-sponsored Banff Knowledge Acquisition for Knowledge-based Systems Workshop (pp. 17.1–17.10).

  • Ramesh, B., & Dhar, V. (1992). Supporting systems development by capturing deliberations during requirements engineering. IEEE Transactions on Software Engineering, 18, 498–510.

    Article  Google Scholar 

  • Richards, D. (1998a). The reuse of knowledge in ripple down rule knowledge based systems (p. 336). University of New South Wales: Sydney.

    Google Scholar 

  • Richards, D. (1998b). Ripple down rules with formal concept analysis: A comparison to personal construct psychology. In Proceedings of the 11th Workshop on Knowledge Acquisition, 1: KAT–4. Banff: SRDG Publications.

  • Richards, D. (2001). Knowledge based system explanation: The ripple down rules alternative. International Journal of Knowledge and Information Systems, 5(1), 2–25.

    Article  Google Scholar 

  • Richards, D., & Busch, P. A. (2000). Measuring, formalising and modelling tacit knowledge. In International congress on intelligent systems and applications (ISA’2000).

  • Richards, D., & Busch, P. A. (2001). Acquiring and applying contextualised tacit knowledge. In Australian conference for knowledge management & intelligent decision support (ACKMIDS’ 2001).

  • Schreiber, G. (1993). Operationalizing models of expertise. In G. Schreiber, B. J. Wielinga & J. Breuker (Eds.), A principled approach to knowledge-based development (p. 457). London: Academic Press.

    Google Scholar 

  • Schreiber, G., Wielinga, B., & Breuker, J. (1993). KADS: A principled approach to knowledge-based system development. Amsterdam: Elsevier.

    Google Scholar 

  • Shaw, M. L. G. (1988). Validation in a knowledge acquisition system with multiple experts. In International conference on fifth generation computer systems (pp. 1259–1266). Tokyo: Springer.

  • Shaw, M. L. G., & Gaines, B. (1992). Kelly’s “geometry of psychological space” and its significance for cognitive modeling. The New Psychologist, 23–31.

  • Siewiorek, D. P., Bell, C. G., & Newell, A. (1982). Computer structures: Principles and examples. New York: McGraw-Hill.

    Google Scholar 

  • Stumme, G., Studer, R., & Sure, Y. (2000). Towards an order-theoretical foundation for maintaining and merging ontologies. In Proceedings of Referenzmodellierung 2000 (pp. 136–149). Aachen: Shaker.

  • Suchman, L. A. (1987). Plans and situated actions: The problem of human-machine communication. New York, NY: Cambridge University Press.

    Google Scholar 

  • Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. Cambridge, MA: A Bradford Book, The MIT Press.

    Google Scholar 

  • Vera, A., & Simon, H. A. (1993a). Situated action: A response to reviewers. Cognitive Science, 17, 77–86.

    Google Scholar 

  • Vera, A., & Simon, H. A. (1993b). Situated action: A symbolic interpretation. Cognitive Science, 17, 7–48.

    Article  Google Scholar 

  • Vera, A., & Simon, H. A. (1993c). Situated action: Reply to William Clancey. Cognitive Science, 17, 117–133.

    Google Scholar 

  • Waldrop, M. M. (1990). Fast, cheap, and out of control. Science, 248, 959–961.

    Article  Google Scholar 

  • Widmer, G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1), 69.

    Google Scholar 

  • Wielinga, B. J., Schreiber, T. A., & Breuker, J. A. (1992). KADS: A modeling approach to knowledge engineering. Knowledge Acquisition, 4(1), 5–54.

    Article  Google Scholar 

  • Wille, R. (1981). Restructuring lattice theory: An approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered sets: Proceedings of the NATO Advanced Study Institute held at Banff, Canada (pp. 445–472). D. Dordrecht: Reidel Publishing.

  • Wille, R. (1989). Knowledge acquisition by methods of formal concept analysis. In E. Diday (Ed.), Data Analysis, Learning Symbolic and Numeric Knowledge (pp. 365–380). New York, NY: Nova Science Publishers.

    Google Scholar 

  • Wille, R. (1992). Concept lattices and conceptual knowledge systems. Computers Mathematical Applications, 23(6), 493–515.

    Article  MATH  Google Scholar 

  • WordNet. (2003). WordNet. Retrieved January 12, 2006, from http://dictionary.reference.com/

Download references

Acknowledgements

The majority of this paper is based on research carried out while affiliated with the Smart Internet Technology Cooperative Research Centre (SITCRC), Bay 8, Suite 9/G12, Australian Technology Park, Eveleigh, NSW 1430 and the School of Computing, University of Tasmania, Locked Bag 100, Hobart, Tasmania.

Author information

Authors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of Ballarat, P.O. Box 663, Ballarat, VIC, 3353, Australia

    Richard Dazeley

  2. School of Computing and Information Systems, University of Tasmania, Private Bag 100, Hobart, Tasmania, 7001, Australia

    Beyong Ho Kang

Authors
  1. Richard Dazeley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beyong Ho Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Richard Dazeley.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dazeley, R., Kang, B.H. Epistemological Approach to the Process of Practice. Minds & Machines 18, 547–567 (2008). https://doi.org/10.1007/s11023-008-9117-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11023-008-9117-3

Keywords

Search

Navigation