>> Btw, I doubt that the fact that you know something is reflected by >> probabilistic logic, without your formalizing it in any way. > > That is =EXACTLY= what Bayesian probability theory is all about !!! > In Bayesian probability theory, _ALL_ probabilities are conditional > on the knowledge base one is willing to apply to the problem at hand. > The conditional probability P(A|{B}) in Bayesian theory is the degree > of confidence one has in the truth of Boolean proposition 'A', given > that the set of Boolean propositions {B} (the knowledge base one is > willing to apply to the problem) is assumed to be true. The Laws of > probability and Bayes theorem provide all the tools one needs to reason > about such `uncertain' Boolean propositions, as well as to incorporate > new data into one's knowledge base. See G. Larry Bretthorst's paper > ``An Introduction To Model Selection Using Probability Theory As Logic'' > <http://bayes.wustl.edu/glb/model.ps.gz>, or the draft of E. T. Jaynes' > magnum opus ``Probability Theory: The Logic of Science'' > <http://bayes.wustl.edu/etj/prob.html>.
Probability theory is related with the question "how often something happened". When in each experiment we get the same results, then this problem is not related with theory of probability. We know the answers with probability one. When I see that knowledge of my students is related with probability, (For the same question I got different response.) then I doubt about their knowledge. Let us consider segment on the plane, which is divided into two parts T /_____ segment T \ T T Top part of the plane T XXXXXXXXXXXXXXXXXX B B B Bottom part of the plane B B Now we can ask the following question. Is the segment at the bottom part of the plane? The answers "NO" or "YES" are not perfect in this case. We can build a non-probabilistic measure which describe this problem. m(segment | bottom)= Length(part B)/Length(part B + part T) In this case m( segments | bottom)=5/10=0.5 I think that this problem is not related with probability. (In each experiment we get the same result.) Because of that in this case we can't apply probabilistic logic. I think that in real word exist uncertain problems which can't be described by probability theory. Andrzej Pownuk --------------------------------------------- MSc. Andrzej Pownuk Chair of Theoretical Mechanics Silesian University of Technology E-mail: pownuk@zeus.polsl.gliwice.pl URL: http://zeus.polsl.gliwice.pl/~pownuk ---------------------------------------------