Typical bottom-up, forward-chaining reasoning systems such as hyperresolution lack goaldirectedness, while typical top-down, backward-chaining reasoning systems like Prolog or model elimination repeatedly solve the same goals. Reasoning systems that are goal-directed and avoid repeatedly solving the same goals can be constructed by formulating the top-down methods meta-theoretically for execution by a bottom-up reasoning system (hence, we use the term upside-down meta-interpretation). This formulation also facilitates the use of flexible search strategies, such as merit-ordered search, that are common to bottom-up reasoning systems. The model elimination theorem-proving procedure, its extension by an assumption rule for abduction, and its restriction to Horn clauses are adapted here for such upside-down meta-interpretation. This work can be regarded as an extension of the magic-sets or Alexander method for query evaluation in deductive databases to both non-Horn clauses and abductive reasoning.
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This research was supported by the National Science Foundation under Grant CCT-8922330 and by the Defense Advanced Research Projects Agency under Office of Naval Research Contract N00014-90-C-0220. The views and conclusions contained herein are those of the author and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the National Science Foundation, the Defense Advanced Research Projects Agency, or the United States Government.
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Stickel, M.E. Upside-down meta-interpretation of the model elimination theorem-proving procedure for deduction and abduction. J Autom Reasoning 13, 189–210 (1994). https://doi.org/10.1007/BF00881955
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