A step toward a universal theory of failure handling

• Autorzy: Obeid N. , Rao R.B.
• Języki publikacji: EN

Abstrakty

EN

We explore, in this paper, some of the fundamental requirements needed for a Universal Theory of Failure Handling. We shall show that dealing with failure touches on our reasoning, predictive, evaluative and judgmental capabilities and thus it requires the ability to reason with incomplete and uncertain temporal information. It also requires reasoning with events before they even happen and about the effect of actions for as long as these are relevant, even if the available time does not permit. There may also be a need for reasoning about the reasoning process itself. We shall discuss the notion of failure with respect to decision-making and knowledge. We give a very brief presentation of Dorner's logic of failure and research into artificial intelligence and its implication for handling failures. We shall propose means of computing the degrees of failure induced by humans and in physical systems. In addition, we shall initiate a discussion on reasoning with failures and put forward a proposal for an integrative and proactive approach to monitoring, diagnosis and learning from failures.

Słowa kluczowe

EN

logic; failure; reasoning; decision-making; learning; diagnosis; monitoring; time; event; nonmonotonic

PL

logika; monitoring; czas; rozumowanie; diagnozowanie; podejmowanie decyzji; uszkodzenie

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2007
• Tom: nr 1(41)
• Strony: 5--14
• Opis fizyczny: Bibliogr. 23 poz.

Twórcy

autor

Obeid N.

autor

Rao R.B.

• Department of Computer Information Systems, King Abdullah School for Information Technology, The University of Jordan, JORDAN

Bibliografia

• 1. Davis, E., (1991), Reasoning Common Sense, San Francisco: Morgan Kaufmann.
• 2. Dorner D, (1997), The Logic of Failure: Recognizing and Avoiding Error in Complex Situations, HarperCollins Publishers.
• 3. Ford, K. M. and Pylyshyn, Z. (eds.), (1996), The Robot's Dilemma Revisited: The Frame Problem in Artificial Intelligence, Norwood, New Jersey: Ablex Publishing Co.
• 4. Genesereth, M. and Nilsson, J., (1987), Logical Foundations of Artificial Intelligence, San Mateo, California: Morgan Kaufmann.
• 5. Giunchiglia E., Kartha G. N. and Lifschitz V., (1997), Representing action: Indeterminacy and ramifications, Artificial Intelligence, Vol. 95, No. 2, 409-438.
• 6. Hare R. M., (1963), Freedom and Reason, Oxford University Press, Oxford.
• 7. Hanks S. and McDermott D., (1987), Nonmonotonic Logic and Temporal Projection, Artificial Intelligence, Vol. 33, 379-412.
• 8. Lifschitz V., (1990), Frames in the space of situations, Artificial Intelligence, Vol. 46, 365-376.
• 9. Lin, F., and Reiter, R., (1994), State constraints revisited, Journal of Logic and Computation, Vol. 4, 655-678.
• 10. McCarthy J., (1982), Circumscription - A Form of Non-Monotonic Reasoning, Artificial Intelligence, Vol. 13, 27-39.
• 11. Mitchell T., (1997), Machine Learning, McGraw Hill.
• 12. Obeid N. and Rao, B.K.N.., (2002), Innovative Trends in Knowledge Based Logical Reasoning in the Field of COMADEM, the Field, International Journal of Condition Monitoring and Diagnostic Engineering Management (COMADEM), Vol. 5, No. 3, 5-13, UK.
• 13. Obeid N. and Rao, B. K. N.., (2004), Diagnostic Temporal Reasoning in Model-Based Diagnosis (MBD) of Dynamic System, International Journal of Condition Monitoring and Diagnostic Engineering Management (COMADEM), Vol. 7, No. 1, 13-28, UK.
• 14. Obeid N. and Rao, B. K. N. (2005), Temporal Aspects in Condition Monitoring & Root Cause Failure Diagnosis of Modern Complex Systems, International Journal of Condition Monitoring and Diagnostic Engineering Management (COMADEM), Vol. 8, No. 3, UK.
• 15. Obeid N., Salah I. and Rao, B. K. N. (2006), The Role of Knowledge Management in Diagnosing & Prognosing System’s Failures. Diagnostyka, No.1 (37), 9 – 16, Poland.
• 16. Pearl J., (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, San Mateo, Calif.: Morgan Kaufmann.
• 17. Pylyshyn, Zenon (ed.), (1987), The Robot's Dilemma: The Frame Problem in Artificial Intelligence, Norwood, New Jersey: Ablex Publishing Co.
• 18. Rao, B.K.N. (2006), Toward the Universal Theory of Failure, International Proceedngs of Condition Monitoring and Diagnostic Engineering Management (COMADEM), Published by Lulea University of Technology, Sweden. Pp. 85 – 101.
• 19. Thielscher M., (1989), Ramification and causality, Artificial Intelligence, Vol. 89, No. 1-2, 317-364.
• 20. Thielscher M., (1996), Causality and the qualification problem, in KR'96: Principles of Knowledge Representation and Reasoning, Luigia Carlucci Aiello, Jon Doyle, and Stuart Shapiro, eds., San Francisco, California: Morgan Kaufmann, 51-62.
• 21. Thielscher M., (2000), Representing the knowledge of a robot, in KR2000: Principles of Knowledge Representation and Reasoning, Anthony G. Cohn, Fausto Giunchiglia, and Bart Selman, eds., San Francisco: Morgan Kaufmann, 109-120.
• 22. Reiter, R., (1980), A logic for default reasoning, Artificial Intelligence, Vol. 13, 81-137.
• 23. Zadeh, L.A. (1973). The Concept of a Linguistic Variable and its Application to Approximate Reasoning, Memorandum ERL-M411, Berkeley, October.