A New Fast Inference Technique based on Knowledge-Base Compilation

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To overcome the problem of knowledge acquisition bottleneck in current knowledge systems, there have been intensive researches on learning and knowledge acquisition support. Unlike these approaches, we have carried out our research from the viewpoint that a fast inference mechanism based on knowledge-base compilation from declarative knowledge is important. If we consider logical knowledge or its subset, i.e., Horn clauses, the knowledge compilation bacomes to the transformation into prime implicates, which are all the implications derivable from the knowledge-base but not subsumed by others. However, the required memory space becomes too large if we compile the entire knowledge-base. Thus we have developed a partial compilation method which compiles the selected portions being expected to contribute the inference efficiency in a hypothetical-reasoning knowledge-base. Moreover, we have combined a multi-level logic-circuit minimization technique with the knowledge compilation to achieve higher efficiency. For the fast inference of the hypothetical reasoning, we have also developed an important polynomial-time inference mechanism employing an approximate solution method of 0-1 integer programming.