%0 Book %1 zadeh86 %A Zadeh, Lotfi %B Uncertainty in Artificial Intelligence %D 1986 %I North-Holland %K ai probabilistic %P 103--115 %T Is Probability Theory Sufficient for Dealing With Uncertainty in AI?

@book{zadeh86, added-at = {2006-03-24T16:34:33.000+0100}, author = {Zadeh, Lotfi}, biburl = {https://www.bibsonomy.org/bibtex/263ac11d10572b0922a39800c24c45ece/neilernst}, citeulike-article-id = {120110}, comment = {question: how to represent it is very likely that Mary is young (fuzzy probability and fuzzy predicate) - since prabability is two-valued, an event either happens or it does not, and there is no room for descriptors like warm, short etc. .A proposition is a description of an event. 'It will be a warm day tomorrow' can be assigned a probability of .6 say, but we ignore the nature of what a warm day is. I agree that warm is definitely fuzzy in definition, but what the Bayesians are saying is that the probability is measuring howmuch someone bleives in that statement, not this abstract value called 'warm' and to what degree that is true. I think Cheeseman answers this, but his explanation seems very complicated, resulting in probabilities of probabilities eg. what is the prob. that A beleives it will be warm tomorrow, what is the prob. that warm means >25 degrees, etc.}, description = {sdasda}, interhash = {ff249468bafe2aef80f5176edb5aa432}, intrahash = {63ac11d10572b0922a39800c24c45ece}, keywords = {ai probabilistic}, pages = {103--115}, priority = {0}, publisher = {North-Holland}, series = {Uncertainty in Artificial Intelligence}, timestamp = {2006-03-24T16:34:33.000+0100}, title = {Is {P}robability {T}heory {S}ufficient for {D}ealing {W}ith {U}ncertainty in {AI}?}, year = 1986 }