
In this article, the contextual discounting of a belief function, a classical discounting generalization, is extended and its particular link with the canonical disjunctive decomposition is highlighted. A general family of correction mechanisms allowing one to weaken the information provided by a source is then introduced, as well as the dual of this family allowing one to strengthen a belief function.
reinforcement, Belief Functions, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 330, discounting, Applied Mathematics, belief functions, canonical decompositions, Reasoning under uncertainty in the context of artificial intelligence, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Theoretical Computer Science, Reinforcement, Artificial Intelligence, Belief functions, Canonical Decompositions, Canonical decompositions, Software, Discounting
reinforcement, Belief Functions, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 330, discounting, Applied Mathematics, belief functions, canonical decompositions, Reasoning under uncertainty in the context of artificial intelligence, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Theoretical Computer Science, Reinforcement, Artificial Intelligence, Belief functions, Canonical Decompositions, Canonical decompositions, Software, Discounting
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