Right. So , how should  What 's the uh , plan ? Like , how should we go about figuring out these
OK. So , first of all is , uh , do e either of you guys , you got a favorite belief - net that you 've , you know , played with ? JavaBayes or something ?
Oh. No , not really.
OK. Well , anyway. f Get one. OK ? So  y so one of th one of the things we wanna do is actually , uh , pick a package , doesn't matter which one , uh , presumably one that 's got good interactive abilities , cuz a lot of what we 're gonna be d You know , we don't need the one that 'll solve massive , uh , belief - nets quickly. d w These are not gonna get big in  in the foreseeable future. But we do want one in which it 's easy to interact with and , uh , modify. Because i that 's  A lot of what it 's gonna be , is , um , playing with this. And probably one in which it 's easy to have , um , what amounts to transcript files. So that if  if we have all these cases  OK ? So we make up cases that have these features , OK , and then you 'd like to be able to say , " OK , here 's a bunch of cases "  There 're even ones tha that you can do learning OK ? So you have all their cases and  and their results and you have a  algorithms to go through and run around trying to set the  the probabilities for you. Um , probably that 's not worth it. I mean , my guess is we aren't gonna have enough data that 's good enough to make the  these data fitting ones worth it , but I don't know. So I would say you guy the first task for you two guys is to um , pick a package. OK , and you wanna it s You know , the standard things you want it stable , you want it  yeah , @ @. And , as soon as we have one , we can start trying to , uh , make a first cut at what 's going on.