B: So I guess this is more or less now just to get you up to date , Johno .
B: This is what , uh ,
C: This is a meeting for me .
B: um ,
B: Eva , Bhaskara , and I did .
D: Did you add more stuff to it ? later ?
B: Um . Why ?
D: Um . I don't know .
D: There were , like , the you know , @ @ and all that stuff .
D: But . I thought you you said you were adding stuff
D: but I don't know .
B: Uh , no .
B: This is B: Um ,
B: Ha !
B: Very nice .
B: Um , so we thought that , We can write up uh , an element ,
B: and for each of the situation nodes that we observed in the Bayes - net ?
B: So .
B: What 's the situation like at the entity that is mentioned ?
B: if we know anything about it ?
B: Is it under construction ?
B: Or is it on fire or something happening to it ?
B: Or is it stable ?
B: and so forth ,
B: going all the way um , f through Parking , Location , Hotel , Car , Restroom , @ @ <comment> Riots , Fairs , Strikes , or Disasters .
C: So is This is A situation are is all the things which can be happening right now ?
C: Or , what is the situation type ?
B: That 's basically just specifying the the input for the w what 's
C: Oh , I see
C: y Why are you specifying it in XML ?
B: Um . Just because it forces us to be specific about the values here ?
C: OK .
B: And , also , I mean , this is a what the input is going to be .
B: Right ?
B: So , we will , uh B: This is a schema . This is C: Well , yeah . I just don't know if this is th l what the Does This is what Java Bayes takes ? as a Bayes - net spec ?
B: No , because I mean if we I mean we 're sure gonna interface to We 're gonna get an XML document from somewhere .
B: Right ?
B: And that XML document will say " We are able to We were able to observe that w the element , um , @ @ <comment> of the Location that the car is near . "
B: So that 's gonna be  <comment> Um .
C: So this is the situational context , everything in it .
C: Is that what Situation is short for ,
C: shi situational context ?
B: Yep .
C: OK .
B: So this is just , again , a an XML schemata which defines a set of possible , uh , permissible XML structures ,
B: which we view as input into the Bayes - net .
B: Right ?
C: And then we can r uh possibly run one of them uh transformations ?
C: That put it into the format that the Bayes n or Java Bayes or whatever wants ?
B: Yea - Are you talking are you talking about the the structure ?
C: Well it B: I mean when you observe a node .
C: When you when you say the input to the v Java Bayes , <comment> it takes a certain format ,
B: Um - hmm .
C: right ?
C: Which I don't think is this .
C: Although I don't know .
B: No , it 's certainly not this .
B: Nuh .
C: So you could just Couldn't you just run a B: XSL . <comment> Yeah .
C: Yeah .
C: To convert it into the Java Bayes for format ?
B: Yep .
C: OK .
B: That 's That 's no problem ,
B: but I even think that , um B: I mean , once Once you have this sort of as running as a module B: Right ?
B: What you want is You wanna say , " OK , give me the posterior probabilities of the Go - there node , when this is happening . "
B: Right ?
B: When the person said this , the car is there , it 's raining , and this is happening .
B: And with this you can specify the what 's happening in the situation , and what 's happening with the user .
B: So we get After we are done , through the Situation we get the User Vector .
B: So , this is a C: So this is just a specification of all the possible inputs ?
B: Yep .
B: And ,
B: all the possible outputs , too .
C: OK .
B: So , we have , um , for example , the , uh , Go - there decision node
B: which has two elements ,
B: going - there and its posterior probability ,
B: and not - going - there and its posterior probability ,
B: because the output is always gonna be all the decision nodes and all the the a all the posterior probabilities for all the values .
C: And then we would just look at the , eh , Struct that we wanna look at
C: in terms of if if we 're only asking about one of the C: So like , if I 'm just interested in the going - there node , I would just pull that information out of the Struct that gets return that would that Java Bayes would output ?
B: Um , pretty much , yes ,
B: but I think it 's a little bit more complex .
B: As , if I understand it correctly , it always gives you all the posterior probabilities for all the values of all decision nodes .
B: So , when we input something , we always get the , uh , posterior probabilities for all of these .
B: Right ?
C: OK .
B: So there is no way of telling it t not to tell us about the EVA values .
C: Yeah , wait I agree ,
C: that 's yeah , use oh , uh Yeah , OK .
B: So so we get this whole list of of , um , things ,
B: and the question is what to do with it ,
B: what to hand on ,
B: how to interpret it ,
B: in a sense .
B: So y you said if you " I 'm only interested in whether he wants to go there or not " , then I just look at that node ,
B: look which one C: Look at that Struct in the output ,
C: right ?
B: Yep .
B: Look at that Struct in the the output ,
B: even though I wouldn't call it a " Struct " .
B: But .
C: Well i well , it 's an XML Structure that 's being res returned ,
B: Oh . Mm - hmm .
C: right ?
B: So every part of a structure is a " Struct " .
B: Yeah .
C: Yeah , I just uh I just was abbreviated it to Struct in my head , and started going with that .
B: That element or object , I would say .
C: Not a C Struct . That 's not what I was trying to k
B: Yeah .
C: though yeah .
B: OK .
B: And , um ,
B: the reason is why I think it 's a little bit more complex or why why we can even think about it as an interesting problem in and of itself is B: Um .
B: So . The , uh B: Let 's look at an example .
C: Well , w wouldn't we just take the structure that 's outputted and then run another transformation on it , that would just dump the one that we wanted out ?
B: Yeah . w We 'd need to prune .
B: Right ?
B: Throw things away .
C: Well , actually , you don't even need to do that with XML .
C: D Can't you just look at one specific B: No
B: Yeah , exactly . The @ @ <comment> Xerxes allows you to say , u " Just give me the value of that , and that , and that . "
B: But , we don't really know what we 're interested in before we look at the complete at at the overall result .
B: So the person said , um , " Where is X ? "
B: and so ,
B: we want to know , um , is Does he want info ?
B: o on this ?
B: or know the location ?
B: Or does he want to go there ?
B: Let 's assume this is our our question .
C: Sure .
B: Nuh ?
B: So .
B: Um .
B: Do this in Perl .
B: So we get B: OK .
B: Let 's assume this is the output .
B: So .
B: We should con be able to conclude from that that B: I mean . It 's always gonna give us a value of how likely we think i it is that he wants to go there and doesn't want to go there ,
B: or how likely it is that he wants to get information .
B: But , maybe w we should just reverse this to make it a little bit more delicate .
B: So , does he wanna know where it is ? or does he wanna go there ?
C: He wants to know where it is .
B: Right .
B: I I I tend to agree .
B: And if it 's If C: Well now , y I mean , you could B: And i if there 's sort of a clear winner here ,
B: and , um and this is pretty , uh indifferent ,
B: then we then we might conclude that he actually wants to just know where , uh t uh , he does want to go there .
C: Uh , out of curiosity , is there a reason why we wouldn't combine these three nodes ? into one smaller subnet ?
C: that would just basically be the question for C: We have " where is X ? " is the question ,
C: right ?
C: That would just be Info - on or Location ?
C: Based upon B: Or Go - there .
B: A lot of people ask that , if they actually just wanna go there .
B: People come up to you on campus and say , " Where 's the library ? "
B: You 're gonna say y you 're gonna say , g " Go down that way . "
B: You 're not gonna say " It 's It 's five hundred yards away from you " or " It 's north of you " , or " it 's located "
C: Well , I mean But the there 's So you just have three decisions for the final node , that would link thes these three nodes in the net together .
B: Um .
B: I don't know whether I understand what you mean .
B: But . Again , in this Given this input , we , also in some situations , may wanna postulate an opinion whether that person wants to go there now
B: the nicest way ,
B: use a cab ,
B: or so s
B: wants to know it wants to know where it is because he wants something fixed there , because he wants to visit t it or whatever .
B: So , it n I mean a All I 'm saying is , whatever our input is , we 're always gonna get the full output .
B: And some some things will always be sort of too not significant enough .
C: Wha
C: Or i or i it 'll be tight .
C: You won't it 'll be hard to decide .
B: Yep .
C: But I mean , I guess I guess the thing is , uh , this is another , smaller , case of reasoning in the case of an uncertainty ,
C: which makes me think Bayes - net should be the way to solve these things .
C: So if you had If for every construction ,
C: right ?
B: Oh !
C: you could say , " Well , there Here 's the Where - Is construction . "
C: And for the Where - Is construction , we know we need to l look at this node , that merges these three things together