PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Set of experience: a knowledge structure for formal decision events

Identyfikatory
Warianty tytułu
Konferencja
Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS'04) / sympozjum [December 2004; Gold Coast, Australia]
Języki publikacji
EN
Abstrakty
EN
When managers have to make a decision, they use previous similar or equal decisions to help themselves in the new decision-making process. Hence, it is very important to keep record of past decisions. For us, every formal decision taken has to be stored as knowledge or/and as an event that has occurred. A technology able to do this will allow us to improve our decision making process, reducing the decision time, as well as avoiding repetition and duplication in the process. Developing a knowledge structure, which would store experience from the day-to-day decision process, as well as allows us to administer that acquired knowledge, will improve the quality of decision-making. We are proposing such a knowledge structure, which is named a Set of Experience. A Set of Experience is a combination of organized information obtained from a formal decision event Fully applied, the Set of Experience knowledge structure could advance the notion of administering knowledge in the current decision making environment.
Rocznik
Tom
Strony
95--113
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
autor
  • Faculty of Engineering and Built Environment, University of Newcastle, University Drive, Australia
  • Faculty of Engineering and Built Environment, University of Newcastle, University Drive, Australia
Bibliografia
  • [1] Awad E., Ghaziri H., Knowledge Management, New Jersey, Pearson Education Inc., Prentice Hall 2004.
  • [2] Chiang A., Trappey A., Ku C.C., Using Knowledge-Based Intelligent Reasoning to Support Dynamic Collaborative Design, in: Fifth Asia Pacific Industrial Engineering and Management Systems Conference, E. Kozan (ed.), Gold Coast, Australia, QUT 2004.
  • [3] Coakes E., Knowledge Management: Current Issues and Challenges, IRM Press, London 2003.
  • [4] Davis R., Shrobe H., Szolovitz P., What is a Knowledge Representation?, AI Magazine, 14, 1(1993), 17-33.
  • [5] Drucker P., The Post-Capitalist Executive: Managing in a Time of Great Change, Penguin, New York 1995.
  • [6] Gerwin D., Tuggle F., Modelling Organizational Decisions Using the Human Problem Solving Paradigm, Academy of Management Review, October (1978), 762-773.
  • [7] Goldratt E.M., Cox J., The Goal, Aldershot, Hants Grover 1986.
  • [8] Holtzman S., Intelligent Decision Systems, Addison-Wesley, Massachusetts 1989.
  • [9] Laskey K., Mahoney S.M., Network fragments: representing knowledge for constructing probabilistic models, in: Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence, 1997.
  • [10] Lee J.K., Song Y.U., Unification of Linear Programming with a Rule-based System by the Post-model Analysis Approach, Management Science, 41 (1995), 835-847.
  • [11] Lloyd J.W., Logic for Learning: Learning Comprehensible Theories from Structure Data, Springer, Berlin 2003.
  • [12] Malhotra Y., From Information Management to Knowledge Management: Beyond the 'Hi-Tech Hidebound' Systems', Knowledge Management for the Information Professional, K. Srikantaiah, M.E.D. Koening (eds.), 2004 (2000), 37-61.
  • [13] Marshall D., Artificial Intelligence Notes, viewed September 2004, (http://www.cs.cfac.uk/Dave/AI2/AI_notes.html) 2004.
  • [14] Maturana H., Varela F., Autopoiesis and Cognition, Reidel, Boston 1980.
  • [15] Minsky M., A Framework for Representing Knowledge, Memorandum 306, AI Laboratory, Massachusetts, Institute of Technology 1974.
  • [16] Nilsson N., Logic and Artificial Intelligence, Artificial Intelligence, 47, 1(1991), 31-56.
  • [17] Noble D., Distributed Situation Assessment, in: Proceedings of FUSION '98 International Conference, 1998.
  • [18] Nonaka I., Takeuchi H., The Knowledge-Creating Company: How Japanese Companies Create The Dynamics Of Innovation, Oxford University Press, New York 1995.
  • [19] Pomerol J.C., Adam F., From Human Decision Making to DMSS Architecture, Decision Making Support Systems: Achievements and Challenges for the New Decade, M. Mora, G. Forgionne, J. Gupta (eds.). Idea Group Inc., London 2003.
  • [20] Ryu W., DNA Computing: A Primer, viewed November 2004, (www.arstechnica.eom/reviews/2q00/dna/dna-1 .html), 2004.
  • [21] Sanin C., Szczerbicki E., Knowledge Supply Chain System: A Conceptual Model, Knowledge Management: Selected Issues, A. Szuwarzynski (ed.), Gdansk University of Technology Press, Gdańsk 2004, 79-97.
  • [22] Schatz B., Mohay, G., Clark A., Rich Event Representation for Computer Forensics, in: Fifth Asia Pacific Industrial Engineering and Management Systems Conference, E. Kozan (ed.), QUT, Gold Coast, Australia 2004.
  • [23] Shaw M., Gaines B., Kelly's "Geometry of Psychological Space" and its Significance for Cognitive Modelling, The New Psychologist, October (1992), 23-31.
  • [24] Szczerbicki E., Information Management: Modelling, Analysis and Simulation Perspective, GTN, Gdańsk 2004.
  • [25] Tsoukas H., Mylonopoulos N., Organizations as Knowledge Systems: Knowledge, Learning and Dynamic Capabilities, Palgrave Macmillan, New York 2004.
  • [26] Xiang Y., Poh K.L., Building Bayesian Network for Probabilistic Situation Assessment, in: Fifth Asia Pacific Industrial Engineering and Management Systems Conference, E. Kozan (ed.), QUT, Gold Coast, Australia 2004.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP1-0078-0089
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.