Imprecise Belief Fusion Facing a DST benchmark problem
This addresses a foundational issue in belief fusion for AI systems dealing with imprecise information, though it appears incremental as it modifies an existing rule rather than introducing a new paradigm.
The paper tackled the anomalous behavior in Dempster-Shafer Theory (DST) belief fusion, where equal agents' opinions can be disregarded, by identifying an isomorphism to Probabilistic Logic and replacing the DST combination rule with a new fusion process to eliminate anomalies.
When we merge information in Dempster-Shafer Theory (DST), we are faced with anomalous behavior: agents with equal expertise and credibility can have their opinion disregarded after resorting to the belief combination rule of this theory. This problem is interesting because belief fusion is an inherent part of dealing with situations where available information is imprecise, as often occurs in Artificial Intelligence. We managed to identify an isomorphism betwin the DST formal apparatus into that of a Probabilistic Logic. Thus, we solved the problematic inputs affair by replacing the DST combination rule with a new fusion process aiming at eliminating anomalies proposed by that rule. We apply the new fusion method to the DST paradox Problem.