| 摘要 |
BiPSA is a novel inferential methodology characterized by: (1) breaking down all issues of unknown and uncertainty to a cascade of binary questions, (2) identifying all available sources of knowledge, and polling each source individually with respect to each binary question in its turn. Each binary answer is associated with a measure of confidence, and is expressed in a range {N:-N}, where N is a natural number. These answers are integrated through a novel minimum-arbitrariness mathematical operation to an output of the same format, that can be treated as input to a subsequent integration thereby allowing for a construction of a network that is capable of re-configuration, responding to feedback, and hence improving the merit and the credibility of the integrated answer. Useful for various situations challenged by uncertainty and partial knowledge, e.g.: R&D, pattern-recognition, inferential image and data technology, human/machine decision-making, and management procedures.
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