Networked bubble propagation method as a polynomial-time hypothetical reasoning for computing quasi-optimal solution

A hypothetical reasoning is an important knowledge system's framework because of its theoretical basis and its usefulness for practical problems including diagnosis, design, etc. One crucial problem with the hypothetical reasoning is, however, its slow inference speed. In order to achieve practical or tractable speed, polynomial-time approximate solution method of equivalent 0-1 integer programming, i.e., pivot and complement method, has been applied to computing hypothetical reasoning. However, to achieve further improvement by considering the knowledge structure of a given problem, it is beneficial to have another inference method which can work in knowledge domain rather than mathematical programming domain. For this purpose, the above method is reformalized in this paper using a new type of network. By taking advantage of the knowledge structure, this network achieves an inference speed in the order of 0(N²) against N, where N indicates problem size.