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The Acquisition of Novel Knowledge by Creative Re-Organizations


Franz Schmalhofer
German Research Center for Artificial Intelligence (DFKI)
Postfach 2080, D-67608 Kaiserslautern, Germany
e-mail: schmalho@dfki.uni-kl.de

James Stuart Aitken
Department of Computing Science
University of Glasgow, Glasgow G128QQ, Scotland
e-mail: stuart@dcs.gla.ac.uk


Position Statement:

For launching innovative products into successful markets, knowledge, its organization and its timely utilization are becoming the most decisive business factors in the currently emerging knowledge society. As Nonaka & Takeuchi (1995) have convincingly pointed out, the companies of the future will live in an environment, where markets are continuously shifting, technology proliferating, competitors multiplying and products will become obsolete overnight. Under such circumstances, where uncertainty is the only certainty, that remains, any successful enterprise must continuously acquire and utilize new knowledge.

However, the knowledge-based system technologies which have been developed in the mainstream of Artificial Intelligence research, such as model-based approaches (Wielinga, Schreiber & Breuker, 1992) and formal ontologies (Gruber, 1993) are not yet sufficiently armed for creatively producing new knowledge that can subsequently be reified into novel products. In the current position paper, it is therefore suggested that human learning and human comprehension, as it has been studied in cognitive science (Schmalhofer, in press), can also serve as a model for the constructive and creative learning processes that may occur in organizations and businesses. In particular, we thereby emphasize creative learning processes (see: Boden, 1991).

Learning by creative inferencing does not only (deductively or inductively) explicate some piece of information that is already implicitly contained in the available information, but instead creates some unexpectedly novel (and possibly quite abstract) statement by merging information from different conceptual spaces. A creative inference thus requires the immersion of information from another conceptual space which is then used (in combination with other information) to construct a novel creative initiative.

Given this presupposition, we describe the EKI system, (EKI = Evolution of Kreative Initiatives). With the EKI system (see Schmalhofer, Franken & Schwerdtner, 1997), the scientists and practitioners of a company can independently advertise within a company their most significant or most favorite competence (as well as cooperation possibilities with colleagues and other departments) in knowledge-bases. The knowledge-bases may for example contain personal skill profiles as well as the actual or desired activities in which a practitioner participates (or wants to participate) in. These knowledge-bases thus contain knowledge-level descriptions of autonomous agents (Aitken et al., 1994) upon which creative inferencing can be performed.

A knowledge manager may then apply the methods of the EKI-tool for analyzing the company's knowledge assets with respect to possible business initiatives. More specifically, by a user-programmable marker-passing process (which is to be programmed by the knowledge manager), possibilities for promising innovations are first identified. Via a compilation process, knowledge maps for enterprise-specific successes can then be generated. These processes should be performed in a way so that there is a maximum compatability to existing enterprise modeling approaches. Ideally the EKI-tool should be integrated with current workflow-modeling technology.

With this analysis, creative initiatives towards promising innovations can subsequently be proposed. Unlike the current wisdom in the field of Artificial Intelligence, solutions to knowledge management problems are thus no longer seen as developing information processing machines (see Gardner, 1987), but instead as providing useful and useable tools to knowledge workers (see: Kidd, 1994) and their creative thoughts so that they will be better able to anticipate the scope of the possible future market successes for their company. In our position presentation, we will show a worked-out example of how the EKI-tool is applied for these purposes. It will thus become clear how the EKI-tool can be applied for achieving a cooperative knowledge-evolution process (see Schmalhofer & Tschaitschian, 1995) and how this process can be guided by future market needs.

References

Aitken, J. S., Schmalhofer, F. & Shadbolt, N. A knowledge level characterisation of multi-agent systems. In: Wooldridge, M.J. & Jennings, N. R. (Eds) Intelligent Agents: Agent Theories, Architectures and Languages, Berlin: Springer-Verlag, (Lecture Notes in Artificial Intelligence 890) pp. 179-190, 1994.

Boden, M. A. (1991) The creative mind: Myths and mechanisms. New York: Basic Books, Inc., Publishers.

Gardner, H. (1987). The mind's new science: A history of the cognitive revolution. New York: Basic Books, Inc., Publishers

Gruber, Th. R. (1993) Towards principles for the design of ontologies used for knowledge sharing. In N. Guarino and R. Poli (Eds) Formal Ontologies in Conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers, in press.

Kidd, A. (1994). The marks are on the Knowledge Worker. Proceedings of CHI-94: Human Factors in Computing Systems, Boston, Mass (24-28 April'94), 186-191. ACM Press: New York.

Nonaka, I., & Tekeuchi, H., (1995). The knowledge-Creating Company. Oxford: University Press.

Schmalhofer, F. (in press) Constructive Knowledge Acquisition: A Computational Model and Experimental Evaluation. Hillsdale: Lawrence Erlbaum Associates.

Schmalhofer, F., Franken, L., & Schwerdtner, J. (1997) A computer tool for constructing and integrating inferences into text representations. Behavior Research Methods, Instruments, & Computers.

Schmalhofer, F.& Tschaitschian, B. (1995) Cooperative knowledge evolution for complex domains. In G. Tecuci & Y. Kodratoff (eds.) Machine Learning and Knowledge Acquisition: Integrated Approaches, London: Academic Press, 145-166.

Wielinga, B. J., Schreiber, A. T., & Breuker, J.A. (1992) KADS: A modeling approach to knowledge engineering. Knowledge Acquisition, 4 (1), 5-53.