Abstract
The efficiency of general belief revision techniques like the TMS or the ATMS decreases rapidly with the size and connectivity of the knowledge base and they are not designed for integrating non-monotonic and probabilistic evidence. Our belief revision algorithm ITMS overcomes these shortcomings by exploiting peculiarities of diagnostic reasoning. It is implemented in the diagnostic shell MED2 with good results.
For each conclusion, a well-founded categorical or probabilistic indicator of its strength is continuously updated. Upon retractation of a justification, the ITMS immediately decides the new state (established, unknown or excluded) of the affected conclusion by checking its remaining strength. The correctness depends on avoiding circular justifications. They can easily be blocked if circular inference paths are precomputed in the knowledge base and the blockage does not manipulate the inference process. Both requirements can be fulfilled in diagnosis.
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© 1987 Springer-Verlag Berlin Heidelberg
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Puppe, F. (1987). Belief Revision in Diagnosis. In: Morik, K. (eds) GWAI-87 11th German Workshop on Artifical Intelligence. Informatik-Fachberichte, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-73005-4_19
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DOI: https://doi.org/10.1007/978-3-642-73005-4_19
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