Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present a solution to Zadeh's Magnus challenge problem on linguistic probabilities. First,  implement Zadeh's solution to this problem. Then,  use the intersection-product syllogism and a syllogism based on the entailment principle to interpret the problem so that it can be solved via Linguistic ighted Averages.  show that the problem can be solved by calculation of pessimistic (lor) and optimistic (upper) probabilities via Linguistic ighted Averages. Then,  choose vocabularies for quantifiers and linguistic probabilities that are involved in the problem statement. The vocabularies are modeled using interval type-2 fuzzy sets.  calculate optimistic (upper) and pessimistic (lor) probabilities, which naturally would be interval type-2 fuzzy sets. Finally,  map the pessimistic and optimistic probabilities to linguistic probabilities present in the vocabularies, so that the results can be comprehended by a human.
['Mohammad Reza Rajati', 'Jerry M. Mendel', 'Dongrui Wu']
I am writing a research paper on a idea Solving Zadeh's Magnus challenge problem on linguistic probabilities via Linguistic Weighted Averages
[{"from": "human", "value": "I am writing a research paper on a idea Solving Zadeh's Magnus challenge problem on linguistic probabilities via Linguistic Weighted Averages"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present a solution to Zadeh's Magnus challenge problem on linguistic probabilities. First,  implement Zadeh's solution to this problem. Then,  use the intersection-product syllogism and a syllogism based on the entailment principle to interpret the problem so that it can be solved via Linguistic ighted Averages.  show that the problem can be solved by calculation of pessimistic (lor) and optimistic (upper) probabilities via Linguistic ighted Averages. Then,  choose vocabularies for quantifiers and linguistic probabilities that are involved in the problem statement. The vocabularies are modeled using interval type-2 fuzzy sets.  calculate optimistic (upper) and pessimistic (lor) probabilities, which naturally would be interval type-2 fuzzy sets. Finally,  map the pessimistic and optimistic probabilities to linguistic probabilities present in the vocabularies, so that the results can be comprehended by a human."}]