Abstract
We present Z-log – a practical system that employs the system-Z [13] semantics. Z-log incurs polynomial cost for compilation and entailment in the horn and q-horn [2] cases. Z-log’s complexity is intractable in the unrestricted case – but intractable in the number of defaults that cause the violation of the q-horn property. We present here initial performance results over two alternative rulesbases. The results indicate that Z-log currently scales to problems on the order of 1000’s of propositional rules when the rules are in q-Horn form. We shall be applying Z-log in cognitive disease diagnosis.
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Minock, M., Kraus, H. (2002). Z-log: Applying System-Z. In: Flesca, S., Greco, S., Ianni, G., Leone, N. (eds) Logics in Artificial Intelligence. JELIA 2002. Lecture Notes in Computer Science(), vol 2424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45757-7_52
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DOI: https://doi.org/10.1007/3-540-45757-7_52
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