Decisional experience: a new approach to intelligent knowledge representation

• Autorzy: Sanin C. , Szczerbicki E.
• Języki publikacji: EN

Abstrakty

EN

We learn through experience. Our brain stores knowledge in terms of keeping our own experience from past situations as well as adding knowledge by learning from experiences of others. All these experiences, over generations, are stored in individual's DNA that carries this information into the future. Our idea is to develop an artificial system, an architecture that would support discovering, adding, storing, improving, and sharing knowledge through experience, in a way similar in some very general sense to what happens in nature. We propose a novel approach in which knowledge is represented by Set of Experience Knowledge Structure (SOEKS), and is carried into the future by Decisional DNA. This paper reports on our efforts at the stage of developing and example of SOEKS.

Słowa kluczowe

EN

knowledge representation; set of experience; decisional DNA

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2009
• Tom: Vol. 35, no 3
• Strony: 63--67
• Opis fizyczny: Bibliogr. 18 poz., rys.

Twórcy

autor

Sanin C.

autor

Szczerbicki E.

• Faculty of Engineering and Built Environment, Callaghan NSW 2308, Australia,

Bibliografia

• [1] Arnold W., Bowie J., Artificial Intelligence: A Personal Commonsense Journey, Prentice Hall, New Jersey, 1985.
• [2] Asif M., Muneer T., Energy supply, its demand and security issues for developed and emerging economies, Renewable Sustain Energy Rev, 111, 2007, pp. 388-413.
• [3] Blakeslee S., Lost on Earth: Wealth of Data Found in Space, New York Times, March 20, C1, 1990.
• [4] Chau K.W., A review on the integration of artificial intelligence into coastal modelling, J. Environ. Manage., 80, 2006, pp. 47-57.
• [5] Chau K.W., A review on integration of artificial intelligence into water quality modelling, Mar. Pollut. Bull., 52, 2006, pp. 726-733.
• [6] Corti L., Backhouse G., Acquiring qualitative data for secondary analysis, Forum: Qualitative Social Research, 6, 2, 2005,
• [7] Feigenbaum E., McCorduck P., The Fifth Generation, Addison-Wesley, Reading, MA, 1983.
• [8] Humphrey C., Preserving research data: A time for action, [in:] Preservation of electronic records: new knowledge and decision-making: postprints of a conference - symposium 2003, Canadian Conservation Institute, Ottawa, 2004, pp. 83-89.
• [9] Johnson P., Who you gonna call? Technicalities, 10, 4, 1990, pp. 6-8.
• [10] Kalogirou S., Artificial intelligence for the modeling and control of combustion processes: a review, Prog. Energy Combust. Sci., 29, 2003, pp. 515-566.
• [11] Kalogirou S., Artificial Intelligence in energy and renewable energy systems, Nova Publisher, New York, 2007.
• [12] Kyung S.P., Soung H.K., Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review, Artif. Intelligence Eng., 12, 1998, pp. 121-134.
• [13] Moghtaderi B., Doroodchi E., Development of a novel power cycle for geothermal energy, REDI Grant, unplublished, 2007.
• [14] Sanin C., Szczerbicki E., Set of Experience: A Knowledge Structure for Formal Decision Events, Foundations of Control and Management Sciences, 3, 2005, pp. 95-113.
• [15] Sanin C., Szczerbicki E., Extending set of experience knowledge structure into a transportable language extensible markup language, International Journal of Cybernetics and Systems, 37(2-3), 2006, pp. 97-117.
• [16] Sanin C., Toro C., Szczerbicki E., An OWL ontology of set of experience knowledge structure, Journal of Universal Computer Science, 13(2), 2007, pp. 209-223.
• [17] Toro C., Sanin C., Szczerbicki E., Posada J., Reflexive Ontologies: Enhancing Ontologies with Self- Contained Queries, International Journal of Cybernetics and Systems, 39(2), 2007, pp. 171-189.
• [18] Zhang Z., Ontology query languages for the semantic Web, Master’s thesis, University of Georgia, Athens, 2005.