%0 Journal Article %1 Halpern1990 %A Halpern, Joseph Y. %D 1990 %J Artificial Intelligence %K %N 3 %P 311 - 350 %R http://dx.doi.org/10.1016/0004-3702(90)90019-V %T An analysis of first-order logics of probability %U http://www.sciencedirect.com/science/article/pii/000437029090019V %V 46 %X We consider two approaches to giving semantics to first-order logics of probability. The first approach puts a probability on the domain, and is appropriate for giving semantics to formulas involving statistical information such as “The probability that a randomly chosen bird flies is greater than 0.9.�? The second approach puts a probability on possible worlds, and is appropriate for giving semantics to formulas describing degrees of belief such as “The probability that Tweety (a particular bird) flies is greater than 0.9.�? We show that the two approaches can be easily combined, allowing us to reason in a straightforward way about statistical information and degrees of belief. We then consider axiomatizing these logics. In general, it can be shown that no complete axiomatization is possible. We provide axiom systems that are sound and complete in cases where a complete axiomatization is possible, showing that they do allow us to capture a great deal of interesting reasoning about probability.

@article{Halpern1990, abstract = {We consider two approaches to giving semantics to first-order logics of probability. The first approach puts a probability on the domain, and is appropriate for giving semantics to formulas involving statistical information such as “The probability that a randomly chosen bird flies is greater than 0.9.�? The second approach puts a probability on possible worlds, and is appropriate for giving semantics to formulas describing degrees of belief such as “The probability that Tweety (a particular bird) flies is greater than 0.9.�? We show that the two approaches can be easily combined, allowing us to reason in a straightforward way about statistical information and degrees of belief. We then consider axiomatizing these logics. In general, it can be shown that no complete axiomatization is possible. We provide axiom systems that are sound and complete in cases where a complete axiomatization is possible, showing that they do allow us to capture a great deal of interesting reasoning about probability. }, added-at = {2014-08-22T17:40:14.000+0200}, author = {Halpern, Joseph Y.}, biburl = {https://www.bibsonomy.org/bibtex/2fbd2b8f25305e90198975186c896b844/junkerm}, doi = {http://dx.doi.org/10.1016/0004-3702(90)90019-V}, file = {Halpern1990.pdf:Halpern1990.pdf:PDF}, groups = {public}, interhash = {e9f3709a7b5b7594e764a16ed35d5ff7}, intrahash = {fbd2b8f25305e90198975186c896b844}, issn = {0004-3702}, journal = {Artificial Intelligence }, keywords = {}, number = 3, pages = {311 - 350}, timestamp = {2014-08-22T17:40:14.000+0200}, title = {An analysis of first-order logics of probability }, url = {http://www.sciencedirect.com/science/article/pii/000437029090019V}, username = {junkerm}, volume = 46, year = 1990 }