AIApr 6, 2017
Transferrable Plausibility Model - A Probabilistic Interpretation of Mathematical Theory of Evidence
arXiv:1704.01742v1
Originality Synthesis-oriented
AI Analysis
This work addresses theoretical foundations in uncertainty reasoning for AI and decision-making, but it appears incremental as it builds on existing Dempster-Shafer theory.
The paper tackles the interpretation of Dempster-Shafer theory by proposing a probabilistic interpretation of plausibility, and it demonstrates a new rule for combining independent evidence while preserving this interpretation.
This paper suggests a new interpretation of the Dempster-Shafer theory in terms of probabilistic interpretation of plausibility. A new rule of combination of independent evidence is shown and its preservation of interpretation is demonstrated.