Meeting transcript:
speaker: B
text: So I guess this is more or less now just to get you up to date , Johno .
speaker: B
text: This is what , uh ,
speaker: C
text: This is a meeting for me .
speaker: B
text: um ,
speaker: B
text: Eva , Bhaskara , and I did .
speaker: D
text: Did you add more stuff to it ? <pause> later ?
speaker: B
text: Um . Why ?
speaker: D
text: Um . I don't know .
speaker: D
text: There were , like , the <disfmarker> you know , @ @ and all that stuff .
speaker: D
text: But . I thought you <disfmarker> you said you were adding stuff
speaker: D
text: but <pause> I don't know .
speaker: B
text: Uh , no .
speaker: B
text: This is <disfmarker>
speaker: B
text: Um ,
speaker: B
text: Ha !
speaker: B
text: Very nice .
speaker: B
text: Um , so we thought that , <vocalsound> We can write up uh , an element ,
speaker: B
text: and <disfmarker> for each of the situation nodes that we observed in the Bayes - net ?
speaker: B
text: So .
speaker: B
text: What 's the situation like at the entity that is mentioned ?
speaker: B
text: if we know anything about it ?
speaker: B
text: Is it under construction ?
speaker: B
text: Or is it on fire or something <pause> happening to it ?
speaker: B
text: Or is it stable ?
speaker: B
text: and so forth ,
speaker: B
text: going all the way um , f through Parking , Location , Hotel , Car , Restroom , @ @ <comment> Riots , Fairs , Strikes , or Disasters .
speaker: C
text: So is <disfmarker> This is <disfmarker> A situation are <disfmarker> is all the things which can be happening right now ?
speaker: C
text: Or , what is the situation type ?
speaker: B
text: That 's basically <pause> just specifying the <disfmarker> the input for the <disfmarker> w what 's
speaker: C
text: Oh , I see
speaker: C
text: y Why are you specifying it in XML ?
speaker: B
text: Um . Just because it forces us to be specific about the values <pause> here ?
speaker: C
text: OK .
speaker: B
text: And , also , I mean , this is a <disfmarker> what the input is going to be .
speaker: B
text: Right ?
speaker: B
text: So , we will , uh <disfmarker>
speaker: B
text: This is a schema . This is <disfmarker>
speaker: C
text: Well , yeah . I just don't know if this is th l what the <disfmarker> Does <disfmarker> This is what Java Bayes takes ? as a Bayes - net spec ?
speaker: B
text: No , because I mean if we <disfmarker> I mean we 're sure gonna interface to <disfmarker> We 're gonna get an XML document from somewhere .
speaker: B
text: Right ?
speaker: B
text: And that XML document will say " We are able to <disfmarker> We were able to observe that w the element , um , @ @ <comment> of the Location that the car is near . "
speaker: B
text: So that 's gonna be <disfmarker> <vocalsound> <comment> Um .
speaker: C
text: So this is the situational context , everything in it .
speaker: C
text: Is that what Situation is short for ,
speaker: C
text: shi situational context ?
speaker: B
text: Yep .
speaker: C
text: OK .
speaker: B
text: So this is just , again , a an XML schemata which defines a set of possible , uh , permissible XML structures ,
speaker: B
text: which we view as input into the Bayes - net .
speaker: B
text: Right ?
speaker: C
text: And then we can r <pause> uh possibly run one of them uh transformations ?
speaker: C
text: That put it into the format that the Bayes n or Java Bayes or whatever wants ?
speaker: B
text: Yea - Are you talking <disfmarker> are you talking about the <disfmarker> the structure ?
speaker: C
text: Well it <disfmarker>
speaker: B
text: I mean when you observe a node .
speaker: C
text: When you <disfmarker> when you say <pause> the input to the <pause> v Java Bayes , <comment> it takes a certain format ,
speaker: B
text: Um - hmm .
speaker: C
text: right ?
speaker: C
text: Which I don't think is this .
speaker: C
text: Although I don't know .
speaker: B
text: No , it 's certainly not this .
speaker: B
text: Nuh .
speaker: C
text: So you could just <disfmarker> Couldn't you just run a <disfmarker>
speaker: B
text: XSL . <comment> Yeah .
speaker: C
text: Yeah .
speaker: C
text: To convert it into the Java Bayes for format ?
speaker: B
text: Yep .
speaker: C
text: OK .
speaker: B
text: That 's <disfmarker> That 's no problem ,
speaker: B
text: but I even think that , um <disfmarker>
speaker: B
text: I mean , once <disfmarker> Once you have this sort of as <disfmarker> running as a module <disfmarker>
speaker: B
text: Right ?
speaker: B
text: What you want is <disfmarker> You wanna say , " OK , give me the posterior probabilities of the Go - there <pause> node , when this is happening . "
speaker: B
text: Right ?
speaker: B
text: When the person said this , the car is there , it 's raining , and this is happening .
speaker: B
text: And with this you can specify the <disfmarker> what 's happening in the situation , and what 's happening with the user .
speaker: B
text: So we get <disfmarker> After we are done , through the Situation we get the User Vector .
speaker: B
text: So , this is a <disfmarker>
speaker: C
text: So this is just a specification of all the possible inputs ?
speaker: B
text: Yep .
speaker: B
text: And ,
speaker: B
text: all the possible outputs , too .
speaker: C
text: OK .
speaker: B
text: So , we have , um , for example , the , uh , Go - there decision node
speaker: B
text: which has two elements ,
speaker: B
text: going - there and its posterior probability ,
speaker: B
text: and not - going - there and its posterior probability ,
speaker: B
text: because the output is always gonna be all the decision nodes and all the <disfmarker> the <disfmarker> a all the posterior probabilities for all the values .
speaker: C
text: And then we would just look at the , eh , Struct that we wanna look at
speaker: C
text: in terms of if <disfmarker> if we 're only asking about one of the <disfmarker>
speaker: C
text: 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 <disfmarker> that Java Bayes would output ?
speaker: B
text: Um , pretty much , yes ,
speaker: B
text: but I think it 's a little bit more complex .
speaker: B
text: As , if I understand it correctly , it always gives you all the posterior probabilities for all the values of all decision nodes .
speaker: B
text: So , when we input something , we always get the , uh , posterior probabilities for all of these .
speaker: B
text: Right ?
speaker: C
text: OK .
speaker: B
text: So there is no way of telling it t not to tell us about the EVA <pause> values .
speaker: C
text: Yeah , wait I agree ,
speaker: C
text: that 's <disfmarker> yeah , use <disfmarker> oh , uh <pause> Yeah , OK .
speaker: B
text: So <disfmarker> so we get this whole list of <disfmarker> of , um , things ,
speaker: B
text: and the question is what to do with it ,
speaker: B
text: what to hand on ,
speaker: B
text: how to interpret it ,
speaker: B
text: in a sense .
speaker: B
text: So y you said if you <disfmarker> " I 'm only interested in whether he wants to go there or not " , then I just look at that node ,
speaker: B
text: look which one <disfmarker>
speaker: C
text: Look at that Struct in the output ,
speaker: C
text: right ?
speaker: B
text: Yep .
speaker: B
text: Look at that Struct in the <disfmarker> the output ,
speaker: B
text: even though I wouldn't call it a " Struct " .
speaker: B
text: But .
speaker: C
text: Well i well , it 's an XML Structure that 's being res returned ,
speaker: B
text: Oh . Mm - hmm .
speaker: C
text: right ?
speaker: B
text: So every part of a structure is a " Struct " .
speaker: B
text: Yeah .
speaker: C
text: Yeah , I just uh <disfmarker> I just was <disfmarker> abbreviated it to Struct in my head , and started going with that .
speaker: B
text: That element or object , I would say .
speaker: C
text: Not a C Struct . That 's not what I was trying to k
speaker: B
text: Yeah .
speaker: C
text: though yeah .
speaker: B
text: OK .
speaker: B
text: And , um ,
speaker: B
text: the reason is <disfmarker> why I think it 's a little bit more complex or why <disfmarker> why we can even think about it as an interesting problem in and of itself is <disfmarker>
speaker: B
text: Um .
speaker: B
text: So . The , uh <disfmarker>
speaker: B
text: Let 's look at an example .
speaker: C
text: 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 ?
speaker: B
text: Yeah . w We 'd need to prune .
speaker: B
text: Right ?
speaker: B
text: Throw things away .
speaker: C
text: Well , actually , you don't even need to do that with XML .
speaker: C
text: D Can't you just look at one specific <disfmarker>
speaker: B
text: No
speaker: B
text: Yeah , exactly . The <disfmarker> @ @ <comment> Xerxes allows you to say , u " Just give me the value of that , and that , and that . "
speaker: B
text: But , we don't really know what we 're interested in <pause> before we look at the complete <disfmarker> at <disfmarker> at the overall result .
speaker: B
text: So the person said , um , " Where is X ? "
speaker: B
text: and so ,
speaker: B
text: we want to know , um , is <disfmarker> Does he want info ?
speaker: B
text: o on this ?
speaker: B
text: or know the location ?
speaker: B
text: Or does he want to go there ?
speaker: B
text: Let 's assume this is our <disfmarker> our question .
speaker: C
text: Sure .
speaker: B
text: Nuh ?
speaker: B
text: So .
speaker: B
text: Um .
speaker: B
text: Do this in Perl .
speaker: B
text: So we get <disfmarker>
speaker: B
text: OK .
speaker: B
text: Let 's assume this is the output .
speaker: B
text: So .
speaker: B
text: We should con be able to conclude from that that <disfmarker>
speaker: B
text: 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 ,
speaker: B
text: or how likely it is that he wants to get information .
speaker: B
text: But , maybe w we should just reverse this to make it a little bit more delicate .
speaker: B
text: So , does he wanna know where it is ? or does he wanna go there ?
speaker: C
text: He wants to know where it is .
speaker: B
text: Right .
speaker: B
text: I <disfmarker> I <disfmarker> I tend to agree .
speaker: B
text: And if it 's <disfmarker> If <disfmarker>
speaker: C
text: Well now , y I mean , you could <disfmarker>
speaker: B
text: And i if there 's sort of a clear winner here ,
speaker: B
text: and , um <disfmarker> and this is pretty , uh <disfmarker> indifferent ,
speaker: B
text: then we <disfmarker> then we might conclude that he actually wants to just know where , uh t uh , he does want to go there .
speaker: C
text: Uh , out of curiosity , is there a reason why we wouldn't combine these three nodes ? into one smaller subnet ?
speaker: C
text: that would just basically be <pause> the question for <disfmarker>
speaker: C
text: We have " where is X ? " is the question ,
speaker: C
text: right ?
speaker: C
text: That would just be Info - on or Location ?
speaker: C
text: Based upon <disfmarker>
speaker: B
text: Or Go - there .
speaker: B
text: A lot of people ask that , if they actually just wanna go there .
speaker: B
text: People come up to you on campus and say , " Where 's the library ? "
speaker: B
text: You 're gonna say <disfmarker> y you 're gonna say , g " Go down that way . "
speaker: B
text: You 're not gonna say " It 's <disfmarker> It 's five hundred yards away from you " or " It 's north of you " , or <disfmarker> " it 's located <disfmarker> "
speaker: C
text: Well , I mean <disfmarker> But the <disfmarker> there 's <disfmarker> So you just have three decisions for the final node , that would link thes these three nodes in the net together .
speaker: B
text: Um .
speaker: B
text: I don't know whether I understand what you mean .
speaker: B
text: But . Again , in this <disfmarker> Given this input , we , also in some situations , may wanna postulate an opinion whether that person wants to go there now
speaker: B
text: the nicest way ,
speaker: B
text: use a cab ,
speaker: B
text: or so s
speaker: B
text: wants to know it <disfmarker> wants to know where it is because he wants something fixed there , because he wants to visit t it or whatever .
speaker: B
text: So , it <disfmarker> n I mean <disfmarker> a All I 'm saying is , whatever our input is , we 're always gonna get the full output .
speaker: B
text: And some <disfmarker> some things will always be sort of too <disfmarker> not significant enough .
speaker: C
text: Wha
speaker: C
text: Or i or i it 'll be tight .
speaker: C
text: You won't <disfmarker> it 'll be hard to decide .
speaker: B
text: Yep .
speaker: C
text: But I mean , I guess <disfmarker> I guess the thing is , uh , this is another , smaller , case of reasoning in the case of an uncertainty ,
speaker: C
text: which makes me think Bayes - net should be the way to solve these things .
speaker: C
text: So if you had <disfmarker> If for every construction ,
speaker: C
text: right ?
speaker: B
text: Oh !
speaker: C
text: you could say , " Well , there <disfmarker> Here 's the Where - Is construction . "
speaker: C
text: And for the Where - Is construction , we know we need to l look at this node , that merges these three things together
speaker: B
text: Mm - hmm .
speaker: C
text: as for th to decide the response .
speaker: C
text: And since we have a finite number of constructions that we can deal with , we could have a finite number of nodes .
speaker: B
text: OK .
speaker: C
text: Say , if we had to y deal with arbitrary language , it wouldn't make any sense to do that ,
speaker: B
text: Mm - hmm .
speaker: C
text: because there 'd be no way to generate the nodes for every possible sentence .
speaker: B
text: Mm - hmm .
speaker: C
text: But since we can only deal with a finite amount of stuff <disfmarker>
speaker: B
text: So , basically , the idea is to f to feed the output of that belief - net into another belief - net .
speaker: C
text: Yeah , so basically take these three things and then put them into another belief - net .
speaker: B
text: But , why <disfmarker> why <disfmarker> why only those three ?
speaker: B
text: Why not the whol
speaker: C
text: Well , I mean , d For the Where - Is question .
speaker: C
text: So we 'd have a node for the Where - Is question .
speaker: B
text: Yeah . But we believe that all the decision nodes are <disfmarker> can be relevant for the Where - Is ,
speaker: B
text: and the Where <disfmarker> How - do - I - get - to
speaker: B
text: or the Tell - me - something - about .
speaker: C
text: You can come in if you want .
speaker: B
text: Yes , it is allowed .
speaker: C
text: As long as y you 're not wearing your h your h headphones .
speaker: C
text: Well , I do I <disfmarker> See , I don't know if this is a <pause> good idea or not .
speaker: C
text: I 'm just throwing it out .
speaker: C
text: But uh , it seems like we could have <disfmarker> I mea or uh we could put all of the all of the r information that could also be relevant <pause> into the Where - Is node answer
speaker: B
text: Mm - hmm .
speaker: B
text: Yep .
speaker: C
text: node
speaker: C
text: thing
speaker: C
text: stuff .
speaker: C
text: And uh <disfmarker>
speaker: D
text: OK .
speaker: B
text: I mean <disfmarker>
speaker: B
text: Let 's not forget we 're gonna get some very strong <pause> input from <pause> these sub dis from these discourse things ,
speaker: B
text: right ?
speaker: B
text: So .
speaker: B
text: " Tell me the location of X . "
speaker: B
text: Nuh ?
speaker: B
text: Or " Where is X located at ? "
speaker: C
text: We u
speaker: B
text: Nuh ?
speaker: C
text: Yeah , I know , but the Bayes - net would be able to <disfmarker> The weights on the <disfmarker> on the nodes in the Bayes - net would be able to do all that ,
speaker: C
text: wouldn't it ?
speaker: B
text: Mm - hmm .
speaker: C
text: Here 's a k
speaker: C
text: Oh !
speaker: C
text: Oh , I 'll wait until you 're <pause> plugged in .
speaker: C
text: Oh , don't sit there .
speaker: C
text: Sit here .
speaker: C
text: You know how you don't like that one .
speaker: C
text: It 's OK .
speaker: C
text: That 's the weird one .
speaker: C
text: That 's the one that 's painful . That hurts . It hurts so bad .
speaker: C
text: I 'm h I 'm happy that they 're recording that .
speaker: C
text: That headphone . The headphone <pause> that you have to put on backwards , with the little <disfmarker> little thing <disfmarker> and the little <disfmarker> little foam block on it ?
speaker: C
text: It 's a painful , painful microphone .
speaker: B
text: I think it 's th called " the Crown " .
speaker: C
text: The crown ?
speaker: D
text: What ?
speaker: B
text: Yeah ,
speaker: B
text: versus " the Sony " .
speaker: A
text: The Crown ?
speaker: A
text: Is that the actual name ?
speaker: B
text: Mm - hmm .
speaker: A
text: OK .
speaker: B
text: The manufacturer .
speaker: C
text: I don't see a manufacturer on it .
speaker: C
text: Oh , wait ,
speaker: B
text: You w
speaker: C
text: here it is .
speaker: C
text: h This thingy .
speaker: C
text: Yeah , it 's " The Crown " .
speaker: C
text: The crown of pain !
speaker: A
text: Yes .
speaker: B
text: You 're on - line ?
speaker: C
text: Are you <disfmarker> are your mike o Is your mike on ?
speaker: A
text: Indeed .
speaker: C
text: OK .
speaker: C
text: So you 've been working with these guys ?
speaker: C
text: You know what 's going on ?
speaker: A
text: Yes ,
speaker: A
text: I have .
speaker: A
text: And , I do .
speaker: A
text: Yeah , alright .
speaker: C
text: Excellent !
speaker: A
text: s So where are we ?
speaker: B
text: We 're discussing this .
speaker: A
text: I don't think it can handle French ,
speaker: A
text: but anyway .
speaker: B
text: So .
speaker: B
text: Assume we have something coming in .
speaker: B
text: A person says , " Where is X ? " ,
speaker: B
text: and we get a certain <disfmarker> We have a Situation vector and a User vector and everything is fine ?
speaker: B
text: An - an and <disfmarker> and our <disfmarker> and our <disfmarker>
speaker: C
text: Did you just sti Did you just stick the m the <disfmarker> the <disfmarker> the microphone actually in the tea ?
speaker: A
text: No .
speaker: B
text: And , um ,
speaker: A
text: I 'm not drinking tea .
speaker: A
text: What are you talking about ?
speaker: C
text: Oh , yeah .
speaker: C
text: Sorry .
speaker: B
text: let 's just assume our Bayes - net just has three decision nodes for the time being .
speaker: B
text: These three , he wants to know something about it , he wants to know where it is , he wants to go there .
speaker: C
text: In terms of , these would be wha how we would answer the question Where - Is ,
speaker: C
text: right ?
speaker: C
text: We u
speaker: C
text: This is <disfmarker> i That 's what you s it seemed like , explained it to me earlier
speaker: C
text: w We <disfmarker> we 're <disfmarker> we wanna know how to answer the question " Where is X ? "
speaker: B
text: Yeah , but , mmm .
speaker: B
text: Yeah .
speaker: B
text: No , I can <disfmarker> I can do the Timing node in here , too ,
speaker: B
text: and say " OK . "
speaker: C
text: Well , yeah , but in the s uh , let 's just deal with the s the simple case of we 're not worrying about timing or anything .
speaker: C
text: We just want to know how we should answer " Where is X ? "
speaker: B
text: OK .
speaker: B
text: And , um ,
speaker: B
text: OK , and ,
speaker: B
text: Go - there has two values ,
speaker: B
text: right ? ,
speaker: B
text: Go - there and not - Go - there .
speaker: B
text: Let 's assume those are the posterior probabilities of that .
speaker: A
text: Mm - hmm .
speaker: B
text: Info - on has True or False and Location .
speaker: B
text: So , he wants to know something about it ,
speaker: B
text: and he wants to know something <disfmarker> he wants to know Where - it - is ,
speaker: A
text: Excuse me .
speaker: B
text: has these values .
speaker: B
text: And , um ,
speaker: C
text: Oh , I see why we can't do that .
speaker: B
text: And , um , in this case we would probably all agree that he wants to go there .
speaker: B
text: Our belief - net thinks he wants to go there ,
speaker: B
text: right ?
speaker: A
text: Yeah .
speaker: A
text: Mm - hmm .
speaker: B
text: In the , uh , whatever ,
speaker: B
text: if we have something like this here ,
speaker: B
text: and
speaker: B
text: this like that
speaker: B
text: and maybe here also some <disfmarker>
speaker: A
text: You should probably <comment> make them out of <disfmarker>
speaker: A
text: Yeah .
speaker: C
text: Well , it
speaker: B
text: something like that ,
speaker: B
text: then we would guess , " Aha ! He , our belief - net , <comment> has s stronger beliefs that he wants to know where it is , than actually wants to go <pause> there . "
speaker: B
text: Right ?
speaker: C
text: That it <disfmarker> Doesn't this assume , though , that they 're evenly weighted ?
speaker: D
text: True .
speaker: C
text: Like <disfmarker>
speaker: C
text: I guess they are evenly weighted .
speaker: A
text: The different decision nodes , you mean ?
speaker: C
text: Yeah , the Go - there , the Info - on , and the Location ?
speaker: A
text: Well , d yeah , this is making the assumption .
speaker: A
text: Yes .
speaker: B
text: What do you mean by " differently weighted " ?
speaker: C
text: Like <disfmarker>
speaker: B
text: They don't feed into anything really anymore .
speaker: C
text: Or I jus
speaker: A
text: But I mean , why do we <disfmarker>
speaker: C
text: Le
speaker: A
text: If we trusted the Go - there node more th much more than we trusted the other ones , then we would conclude , even in this situation , that he wanted to go there .
speaker: A
text: So , in that sense , we weight them equally
speaker: B
text: OK .
speaker: A
text: right now .
speaker: B
text: Makes sense .
speaker: B
text: Yeah .
speaker: C
text: So the But I guess the k the question <disfmarker> that I was as er wondering or maybe Robert was proposing to me is <disfmarker>
speaker: B
text: But <disfmarker>
speaker: C
text: How do we d make the decision on <disfmarker> as to <disfmarker> which one to listen to ?
speaker: A
text: Yeah , so , the final d decision is the combination of these three .
speaker: A
text: So again , it 's <disfmarker> it 's some kind of , uh <disfmarker>
speaker: C
text: Bayes - net .
speaker: A
text: Yeah , sure .
speaker: C
text: OK so , then , the question i So then my question is t to you then , would be <disfmarker>
speaker: C
text: So is the only r reason we can make all these smaller Bayes - nets , because we know we can only deal with a finite set of constructions ?
speaker: C
text: Cuz oth If we 're just taking arbitrary language in , we couldn't have a node for every possible question ,
speaker: C
text: you know ?
speaker: A
text: A decision node for every possible question , you mean ?
speaker: C
text: Well , I <disfmarker> like , in the case of <disfmarker> Yeah .
speaker: C
text: In the ca Any piece of language , we wouldn't be able to answer it with this system , b if we just h
speaker: C
text: Cuz we wouldn't have the correct node .
speaker: C
text: Basically , w what you 're s proposing is a n Where - Is node ,
speaker: C
text: right ?
speaker: A
text: Yeah .
speaker: C
text: And <disfmarker> and if we <disfmarker> And if someone <disfmarker> says , you know , uh , something in Mandarin to the system , we 'd - wouldn't know which node to look at to answer that question ,
speaker: A
text: So is <disfmarker>
speaker: A
text: Yeah .
speaker: C
text: right ?
speaker: A
text: Yeah .
speaker: B
text: Mmm ?
speaker: C
text: So , but <disfmarker> but if we have a finite <disfmarker>
speaker: C
text: What ?
speaker: B
text: I don't see your point .
speaker: B
text: What <disfmarker> what <disfmarker> what I am thinking , or what we 're about to propose here is we 're always gonna get the whole list of values and their posterior probabilities .
speaker: B
text: And now we need an expert system or belief - net or something that interprets that ,
speaker: B
text: that looks at all the values and says , " The winner is Timing .
speaker: B
text: Now , go there . "
speaker: B
text: " Uh , go there , Timing , now . "
speaker: B
text: Or , " The winner is Info - on , Function - Off . "
speaker: B
text: So , he wants to know <pause> something about it , and what it does .
speaker: B
text: Nuh ?
speaker: B
text: Uh , regardless of <disfmarker> of <disfmarker> of the input . Wh - Regardle
speaker: C
text: Yeah , but But how does the expert <disfmarker> but how does the expert system know <disfmarker> how who which one to declare the winner , if it doesn't know the question it is , and how that question should be answered ?
speaker: B
text: Based on the k what the question was , so what the discourse , the ontology , the situation and the user model gave us , we came up with these values for these decisions .
speaker: C
text: Yeah I know . But how do we weight what we get out ?
speaker: C
text: As , which one i Which ones are important ?
speaker: C
text: So my i So , if we were to it with a Bayes - net , we 'd have to have a node <disfmarker> for every question that we knew how to deal with ,
speaker: C
text: that would take all of the inputs and weight them appropriately for that question .
speaker: B
text: Mm - hmm .
speaker: C
text: Does that make sense ?
speaker: C
text: Yay ,
speaker: C
text: nay ?
speaker: A
text: Um , I mean , are you saying that , what happens if you try to scale this up to the situation , or are we just dealing with arbitrary language ?
speaker: C
text: We <disfmarker>
speaker: A
text: Is that your point ?
speaker: C
text: Well , no . I <disfmarker> I guess my question is , Is the reason that we can make a node f
speaker: C
text: or <disfmarker> OK . So , lemme see if I 'm confused .
speaker: C
text: Are we going to make a node for every question ?
speaker: C
text: Does that make sense ? <disfmarker>
speaker: C
text: Or not .
speaker: A
text: For every question ?
speaker: A
text: Like <disfmarker>
speaker: C
text: Every construction .
speaker: A
text: Hmm .
speaker: A
text: I don't <disfmarker> Not necessarily , I would think .
speaker: A
text: I mean , it 's not based on constructions ,
speaker: A
text: it 's based on things like , uh , there 's gonna be a node for Go - there or not , and there 's gonna be a node for Enter , View , Approach .
speaker: C
text: Wel
speaker: C
text: W OK .
speaker: C
text: So , someone asked a question .
speaker: A
text: Yeah .
speaker: C
text: How do we decide how to answer it ?
speaker: B
text: Well , look at <disfmarker> look <disfmarker> Face yourself with this pr question .
speaker: B
text: You get this <disfmarker> You 'll have <disfmarker> y This is what you get .
speaker: B
text: And now you have to make a decision . What do we think ?
speaker: B
text: What does this tell us ?
speaker: B
text: And not knowing what was asked , and what happened , and whether the person was a tourist or a local ,
speaker: B
text: because all of these factors have presumably already gone into making these posterior probabilities .
speaker: C
text: Yeah .
speaker: B
text: What <disfmarker> what we need is a <disfmarker> just a mechanism that says , " Aha ! There is <disfmarker> "
speaker: C
text: I just don't think a " winner - take - all " type of thing is the <disfmarker>
speaker: A
text: I mean , in general , like , we won't just have those three ,
speaker: A
text: right ?
speaker: A
text: We 'll have , uh , like , many , many nodes .
speaker: B
text: Yep .
speaker: A
text: So we have to , like <disfmarker> So that it 's no longer possible to just look at the nodes themselves and figure out what the person is trying to say .
speaker: B
text: Because there are interdependencies ,
speaker: B
text: right ?
speaker: B
text: The uh <disfmarker>
speaker: B
text: Uh , no . So if <disfmarker> if for example , the Go - there posterior possibility is so high , um ,
speaker: B
text: uh , w if it 's <disfmarker> if it has reached <disfmarker> reached a certain height , then all of this becomes irrelevant .
speaker: B
text: So . If <disfmarker> even if <disfmarker> if the function or the history or something is scoring pretty good on the true node , true value <disfmarker>
speaker: C
text: Wel
speaker: C
text: I don't know about that ,
speaker: C
text: cuz that would suggest that <disfmarker> I mean <disfmarker>
speaker: B
text: He wants to go there and know something about it ?
speaker: C
text: Do they have to be mutual
speaker: C
text: Yeah . Do they have to be mutually exclusive ?
speaker: B
text: I think to some extent they are .
speaker: B
text: Or maybe they 're not .
speaker: C
text: Cuz I , uh <disfmarker> The way you describe what they meant , they weren't mutu uh , they didn't seem mutually exclusive to me .
speaker: B
text: Well , if he doesn't want to go there , even if the Enter posterior proba
speaker: B
text: So .
speaker: C
text: Wel
speaker: B
text: Go - there is No .
speaker: B
text: Enter is High ,
speaker: B
text: and Info - on is High .
speaker: C
text: Well , yeah , just out of the other three , though , that you had in the <disfmarker>
speaker: B
text: Hmm ?
speaker: C
text: those three nodes .
speaker: C
text: The - d They didn't seem like they were mutually exclusive .
speaker: B
text: No , there 's <disfmarker> No .
speaker: B
text: But <disfmarker>
speaker: B
text: It 's through the <disfmarker>
speaker: C
text: So th s so , yeah , but some <disfmarker> So , some things would drop out , and some things would still be important .
speaker: B
text: Mm - hmm .
speaker: C
text: But I guess what 's confusing me is , if we have a Bayes - net to deal w another Bayes - net to deal with this stuff ,
speaker: A
text: Mm - hmm .
speaker: C
text: you know ,
speaker: C
text: uh ,
speaker: C
text: is the only reason <disfmarker> OK , so , I guess , if we have a Ba - another Bayes - net to deal with this stuff , the only r reason <pause> we can design it is cuz we know what each question is asking ?
speaker: A
text: Yeah .
speaker: A
text: I think that 's true .
speaker: C
text: And then , so , the only reason <disfmarker> way we would know what question he 's asking is based upon <disfmarker>
speaker: C
text: Oh , so if <disfmarker> Let 's say I had a construction parser , and I plug this in , I would know what each construction <disfmarker> the communicative intent of the construction was
speaker: A
text: Mm - hmm .
speaker: C
text: and so then I would know how to weight the nodes appropriately , in response .
speaker: C
text: So no matter what they said , if I could map it onto a Where - Is construction , I could say , " ah !
speaker: A
text: Ge
speaker: A
text: Mm - hmm .
speaker: C
text: well the the intent , here , was Where - Is " ,
speaker: A
text: OK , right .
speaker: C
text: and I could look at those .
speaker: A
text: Yeah .
speaker: A
text: Yes , I mean .
speaker: A
text: Sure .
speaker: A
text: You do need to know <disfmarker> I mean , to have that kind of information .
speaker: B
text: Hmm .
speaker: B
text: Yeah , I 'm also agreeing that <pause> a simple
speaker: B
text: pru <comment> Take the ones where we have a clear winner .
speaker: B
text: Forget about the ones where it 's all sort of middle ground .
speaker: B
text: Prune those out
speaker: B
text: and just hand over the ones where we have a winner .
speaker: B
text: Yeah , because that would be the easiest way .
speaker: B
text: We just compose as an output an XML mes <vocalsound> message that says . " Go there <pause> now . " " Enter historical information . "
speaker: B
text: And not care whether that 's consistent with anything .
speaker: B
text: Right ?
speaker: B
text: But in this case if we say , " definitely he doesn't want to go there . He just wants to know where it is . "
speaker: B
text: or let 's call this <disfmarker> this " Look - At - H "
speaker: B
text: He wants to know something about the history of .
speaker: B
text: So he said , " Tell me something about the history of that . "
speaker: B
text: Now , the e But for some reason the Endpoint - Approach gets a really high score , <pause> too .
speaker: B
text: We can't expect this to be sort of at O point <comment> three , three , three , O point , three , three , three , O point , three , three , three .
speaker: B
text: Right ?
speaker: B
text: Somebody needs to zap that .
speaker: B
text: You know ?
speaker: B
text: Or know <disfmarker> There needs to be some knowledge that <disfmarker>
speaker: C
text: We <disfmarker> Yeah , but , the Bayes - net that would merge <disfmarker>
speaker: C
text: I just realized that I had my hand in between my mouth and my micr er , my and my microphone .
speaker: C
text: So then , the Bayes - net that would merge there , that would make the decision between Go - there , Info - on , and Location , would have a node to tell you which one of those three you wanted ,
speaker: C
text: and based upon that node , then you would look at the other stuff .
speaker: B
text: Yep .
speaker: C
text: I mean , it i
speaker: B
text: Yep .
speaker: C
text: Does that make sense ?
speaker: B
text: Yep . It 's sort of one of those , that 's <disfmarker> It 's more like a decision tree , if <disfmarker> if you want .
speaker: B
text: You first look o at the lowball ones ,
speaker: C
text: Yeah , i
speaker: B
text: and then <disfmarker>
speaker: C
text: Yeah , I didn't intend to say that every possible <disfmarker>
speaker: C
text: OK .
speaker: C
text: There was a confusion there ,
speaker: C
text: k I didn't intend to say every possible thing should go into the Bayes - net ,
speaker: C
text: because some of the things aren't relevant in the Bayes - net for a specific question .
speaker: C
text: Like the Endpoint is not necessarily relevant in the Bayes - net for Where - Is until after you 've decided whether you wanna go there or not .
speaker: B
text: Mm - hmm .
speaker: A
text: Right .
speaker: C
text: Show us the way , Bhaskara .
speaker: A
text: I guess the other thing is that um , yeah .
speaker: A
text: I mean , when you 're asked a specific question and you don't even <disfmarker>
speaker: A
text: Like , if you 're asked a Where - Is question , you may not even look <disfmarker> like , ask for the posterior probability of the , uh , EVA node ,
speaker: A
text: right ?
speaker: A
text: Cuz , that 's what <disfmarker> I mean , in the Bayes - net you always ask for the posterior probability of a specific node .
speaker: A
text: So , I mean ,
speaker: A
text: you may not even bother to compute things you don't need .
speaker: B
text: Um .
speaker: B
text: Aren't we always computing all ?
speaker: A
text: No .
speaker: A
text: You can compute , uh , the posterior probability of one subset of the nodes , given some other nodes ,
speaker: A
text: but totally ignore some other nodes , also .
speaker: A
text: Basically , things you ignore get marginalized over .
speaker: B
text: Yeah , but that 's <disfmarker> that 's just shifting the problem .
speaker: B
text: Then you would have to make a decision ,
speaker: B
text: " OK , if it 's a Where - Is question , which decision nodes do I query ? "
speaker: A
text: Yeah . So you have to make <disfmarker>
speaker: A
text: Yeah .
speaker: A
text: Yes .
speaker: B
text: That 's un
speaker: A
text: But I would think that 's what you want to do .
speaker: A
text: Right ?
speaker: B
text: Mmm .
speaker: D
text: Well , eventually , you still have to pick out which ones you look at .
speaker: B
text: Yeah .
speaker: D
text: So it 's pretty much the same problem ,
speaker: B
text: Yeah <disfmarker> it 's <disfmarker> it 's <disfmarker> it 's apples and oranges .
speaker: D
text: isn't it ?
speaker: B
text: Nuh ?
speaker: B
text: I mean , maybe it does make a difference in terms of performance , computational time .
speaker: B
text: So either you always have it compute all the posterior possibilities for all the values for all nodes , and then prune the ones you think that are irrelevant ,
speaker: A
text: Mm - hmm .
speaker: A
text: Mmm .
speaker: B
text: or you just make a p @ @ <comment> a priori estimate of what you think might be relevant and query those .
speaker: A
text: Yeah .
speaker: C
text: So basically , you 'd have a decision tree <pause> query , <pause> Go - there .
speaker: C
text: If k if that 's false , query this one . If that 's true , query that one .
speaker: C
text: And just basically do a binary search through the <disfmarker> ?
speaker: A
text: I don't know if it would necessarily be that , uh , complicated .
speaker: A
text: But , uh <disfmarker>
speaker: C
text: Well , in the case of Go - there , it would be .
speaker: A
text: I mean , it w
speaker: C
text: In the case <disfmarker>
speaker: C
text: Cuz if you needed an If y If Go - there was true , you 'd wanna know what endpoint was .
speaker: C
text: And if it was false , you 'd wanna d look at either Lo - Income Info - on or History .
speaker: A
text: Yeah .
speaker: A
text: That 's true , I guess .
speaker: A
text: Yeah , <vocalsound> so , in a way you would have that .
speaker: C
text: Also , I 'm somewhat boggled by that Hugin software .
speaker: A
text: OK , why 's that ?
speaker: C
text: I can't figure out how to get the probabilities into it .
speaker: C
text: Like , I 'd look at <disfmarker>
speaker: A
text: Mm - hmm .
speaker: C
text: It 's somewha It 's boggling me .
speaker: A
text: OK .
speaker: A
text: Alright .
speaker: A
text: Well , hopefully it 's <pause> fixable .
speaker: C
text: Ju
speaker: C
text: Oh yeah , yeah . I d I just think I haven't figured out what <disfmarker> the terms in Hugin mean , versus what Java Bayes terms are .
speaker: A
text: It 's <disfmarker> there 's a <disfmarker>
speaker: A
text: OK .
speaker: B
text: Um , by the way , are <disfmarker> Do we know whether Jerry and Nancy are coming ?
speaker: A
text: So we can figure this out .
speaker: B
text: Or <disfmarker> ?
speaker: A
text: They should come when they 're done their stuff , basically , whenever that is .
speaker: A
text: So .
speaker: C
text: What d what do they need to do left ?
speaker: A
text: Um ,
speaker: A
text: I guess , Jerry needs to enter marks ,
speaker: A
text: but I don't know if he 's gonna do that now or later .
speaker: A
text: But , uh , if he 's gonna enter marks , it 's gonna take him awhile , I guess ,
speaker: A
text: and he won't be here .
speaker: C
text: And what 's Nancy doing ?
speaker: A
text: Nancy ?
speaker: A
text: Um , she was sorta finishing up the , uh , calculation of marks and assigning of grades ,
speaker: A
text: but I don't know if she should be here .
speaker: A
text: Well <disfmarker> or , she should be free after that ,
speaker: A
text: so <disfmarker>
speaker: A
text: assuming she 's coming to this meeting .
speaker: A
text: I don't know if she knows about it .
speaker: C
text: She 's on the email list ,
speaker: C
text: right ?
speaker: A
text: Is she ?
speaker: A
text: OK .
speaker: B
text: Mm - hmm .
speaker: B
text: OK .
speaker: B
text: Because
speaker: B
text: basically , what <disfmarker> where we also have decided , prior to this meeting is that we would have a rerun of the three of us sitting together
speaker: D
text: OK .
speaker: B
text: sometime <pause> this week <pause> again
speaker: A
text: OK .
speaker: B
text: and finish up the , uh , values of this .
speaker: B
text: So we have , uh <disfmarker> Believe it or not , we have all the bottom ones here .
speaker: C
text: Well , I <disfmarker>
speaker: D
text: You added a bunch of <pause> nodes , for <disfmarker> ?
speaker: B
text: Yep .
speaker: D
text: OK .
speaker: B
text: We <disfmarker> we <disfmarker> we have <disfmarker> Actually what we have is this line .
speaker: B
text: Right ?
speaker: C
text: Uh , what do the , uh , structures do ?
speaker: B
text: Hmm ?
speaker: C
text: So the <disfmarker> the <disfmarker> the <disfmarker> For instance , this Location node 's got two inputs ,
speaker: C
text: that one you <disfmarker>
speaker: A
text: Four inputs .
speaker: B
text: Hmm .
speaker: B
text: Four .
speaker: A
text: Those are <disfmarker> The bottom things are inputs , also .
speaker: C
text: Oh , I see .
speaker: A
text: Yeah .
speaker: C
text: OK , that was
speaker: C
text: OK . That makes a lot more sense to me now .
speaker: B
text: Yep .
speaker: C
text: Cuz I thought it was like , that one in Stuart 's book about , you know , the <disfmarker>
speaker: A
text: Alarm in the dog ?
speaker: C
text: U Yeah .
speaker: A
text: Yeah .
speaker: C
text: Or the earthquake and the alarm .
speaker: A
text: Sorry . Yeah , I 'm confusing two .
speaker: C
text: Yeah , there 's a dog one , too ,
speaker: C
text: but that 's in Java Bayes ,
speaker: C
text: isn't it ?
speaker: A
text: Right .
speaker: A
text: Maybe .
speaker: C
text: But there 's something about bowel problems or something with the dog .
speaker: A
text: Yeah .
speaker: B
text: And we have all the top ones ,
speaker: B
text: all the ones to which no arrows are pointing .
speaker: B
text: What we 're missing are the <disfmarker> these ,
speaker: B
text: where arrows are pointing ,
speaker: B
text: where we 're combining top ones .
speaker: B
text: So , we have to come up with values for this , and this , this , this , and so forth .
speaker: B
text: And maybe just fiddle around with it a little bit more .
speaker: B
text: And , um .
speaker: B
text: And then it 's just , uh , edges ,
speaker: B
text: many of
speaker: B
text: edges .
speaker: B
text: And , um , we won't <comment> meet next Monday .
speaker: B
text: So .
speaker: C
text: Cuz of Memorial Day ?
speaker: B
text: Yep .
speaker: A
text: We 'll meet next Tuesday , I guess .
speaker: B
text: Yeah .
speaker: C
text: When 's Jerry leaving for <disfmarker> Italia ?
speaker: B
text: On <disfmarker> on Friday .
speaker: A
text: Which Friday ?
speaker: B
text: This <disfmarker> this Friday .
speaker: A
text: OK .
speaker: D
text: Oh . This Friday ?
speaker: C
text: Ugh .
speaker: B
text: This Friday .
speaker: C
text: As in , four days ?
speaker: B
text: Yep .
speaker: C
text: Or , three days ?
speaker: A
text: Is he <disfmarker> How long is he gone for ?
speaker: B
text: Two weeks .
speaker: A
text: Italy , huh ?
speaker: A
text: What 's , uh <disfmarker> what 's there ?
speaker: B
text: Well , it 's a country .
speaker: B
text: Buildings .
speaker: B
text: People .
speaker: C
text: But it 's not a conference or anything .
speaker: A
text: Pasta .
speaker: C
text: He 's just visiting .
speaker: B
text: Hmm ?
speaker: A
text: Right .
speaker: A
text: Just visiting .
speaker: B
text: Vacation .
speaker: A
text: It 's a pretty nice place ,
speaker: A
text: in my brief , uh , encounter with it .
speaker: B
text: Do you guys <disfmarker>
speaker: B
text: Oh , yeah . So . Part of what we actually want to do is sort of schedule out what we want to surprise him with when <disfmarker> when he comes back .
speaker: B
text: Um , so <disfmarker>
speaker: C
text: Oh , I think we should disappoint him .
speaker: B
text: Yeah ?
speaker: B
text: You <disfmarker> or have a finished construction parser and a working belief - net ,
speaker: B
text: and uh <disfmarker>
speaker: C
text: That wouldn't be disappointing .
speaker: C
text: I think w we should do absolutely no work for the two weeks that he 's gone .
speaker: B
text: Well , that 's actually what I had planned , personally .
speaker: B
text: I had <disfmarker> I <disfmarker> I had sort of scheduled out in my mind that you guys do a lot of work , and I do nothing .
speaker: B
text: And then , I sort of <disfmarker>
speaker: C
text: Oh , yeah , that sounds good , too .
speaker: B
text: sort of bask in <disfmarker> in your glory .
speaker: B
text: But , uh , i do you guys have any vacation plans ,
speaker: B
text: because I myself am going to be , um , gone ,
speaker: B
text: but this is actually not really important .
speaker: B
text: Just this weekend we 're going camping .
speaker: C
text: Yeah , I 'm wanna be this <disfmarker> gone this weekend , too .
speaker: B
text: Ah .
speaker: B
text: But we 're all going to be here on Tuesday again ?
speaker: B
text: Looks like it ?
speaker: D
text: Yeah .
speaker: B
text: OK , then . Let 's meet <disfmarker> meet again next Tuesday .
speaker: B
text: And , um , finish up this Bayes - net .
speaker: B
text: And once we have finished it , I guess we can , um <disfmarker>
speaker: B
text: and that 's going to be more just you and me ,
speaker: B
text: because Bhaskara is doing probabilistic , recursive , structured , object - oriented , uh ,
speaker: C
text: Killing machines !
speaker: B
text: reasoning machines .
speaker: A
text: Yes .
speaker: B
text: And , um <disfmarker>
speaker: C
text: Killing , reasoning . What 's the difference ?
speaker: D
text: Wait . So you 're saying , next Tuesday , is it the whole group meeting ,
speaker: D
text: or just us three working on it , or <disfmarker> or <disfmarker> ?
speaker: B
text: Uh .
speaker: B
text: The whole group .
speaker: B
text: And we present our results ,
speaker: B
text: our final ,
speaker: D
text: OK .
speaker: B
text: definite <disfmarker>
speaker: D
text: So , when you were saying we <pause> need to do a re - run of , like <disfmarker>
speaker: A
text: h What ?
speaker: D
text: What <disfmarker> Like , just working out the rest of the <disfmarker>
speaker: B
text: Yeah . We should do this th the upcoming days .
speaker: D
text: This week ?
speaker: B
text: So , this week , yeah .
speaker: D
text: OK .
speaker: C
text: When you say , " the whole group " , you mean <pause> the four of us , and Keith ?
speaker: B
text: And , Ami might .
speaker: C
text: Ami might be here ,
speaker: C
text: and it 's possible that Nancy 'll be here ?
speaker: B
text: Yep .
speaker: C
text: So ,
speaker: C
text: yeah .
speaker: B
text: Because , th you know ,
speaker: B
text: once we have the belief - net done <disfmarker>
speaker: C
text: You 're just gonna have to explain it to me , then , on Tuesday ,
speaker: C
text: how it 's all gonna work out .
speaker: C
text: You know .
speaker: B
text: We will .
speaker: B
text: OK . Because then , once we have it sort of up and running , then we can start you know , defining the interfaces
speaker: B
text: and then feed stuff into it
speaker: B
text: and get stuff out of it ,
speaker: B
text: and then hook it up to some fake construction parser
speaker: B
text: and <disfmarker>
speaker: C
text: That you will have in about nine months or so .
speaker: C
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: And , um ,
speaker: C
text: The first bad version 'll be done in nine months .
speaker: B
text: Yeah , I can worry about the ontology interface
speaker: B
text: and you can <disfmarker> Keith can worry about the discourse .
speaker: B
text: I mean , this is pretty <disfmarker> Um , I mean , I <disfmarker> I <disfmarker> I hope everybody uh knows that these are just going to be uh dummy values ,
speaker: B
text: right ?
speaker: A
text: Which <disfmarker>
speaker: B
text: where the <disfmarker>
speaker: A
text: Which ones ?
speaker: B
text: S so <disfmarker> so if the endpoint <disfmarker> if the Go - there is Yes and No , then Go - there - discourse will just be fifty - fifty .
speaker: B
text: Right ?
speaker: A
text: Um , what do you mean ? If the Go - there says No , then the Go - there is <disfmarker>
speaker: D
text: I don't get it .
speaker: A
text: I don't u understand .
speaker: B
text: Um .
speaker: A
text: Like , the Go - there depends on all those four things .
speaker: B
text: Yep .
speaker: A
text: Yeah .
speaker: B
text: But , what are the values of the Go - there - discourse ?
speaker: A
text: Well , it depends on the situation .
speaker: A
text: If the discourse is strongly indicating that <disfmarker>
speaker: B
text: Yeah , but , uh , we have no discourse input .
speaker: A
text: Oh , I see . The d See , uh , specifically in our situation , D and O are gonna be , uh <disfmarker>
speaker: A
text: Yeah .
speaker: A
text: Sure .
speaker: A
text: So , whatever .
speaker: D
text: So , so far we have <disfmarker>
speaker: D
text: Is that what the Keith node is ?
speaker: B
text: Yep .
speaker: D
text: OK . And you 're taking it out ? <pause> for now ?
speaker: D
text: Or <disfmarker> ?
speaker: B
text: Well , this is D <disfmarker>
speaker: B
text: OK , this , I can <disfmarker> I can get it in here .
speaker: D
text: All the D 's are <disfmarker>
speaker: B
text: I can get it in here ,
speaker: B
text: so th We have the , uh , um ,
speaker: B
text: sk let 's <disfmarker> let 's call it " Keith - Johno
speaker: B
text: node " .
speaker: A
text: Johno ?
speaker: B
text: There is an H <comment> somewhere printed .
speaker: C
text: There you go .
speaker: A
text: Yeah . People have the same problem with my name .
speaker: B
text: Yeah .
speaker: A
text: Oops .
speaker: B
text: And , um ,
speaker: C
text: Does th th does the H go b before the A or after the A ?
speaker: A
text: Oh , in my name ?
speaker: C
text: Yeah .
speaker: A
text: Before the A .
speaker: C
text: OK , good .
speaker: C
text: Cuz you kn When you said people have the same problem , I thought <disfmarker>
speaker: C
text: Cuz my H goes after the uh e e e the v
speaker: A
text: People have the inverse problem with my name .
speaker: C
text: OK .
speaker: C
text: I always have to check , every time y I send you an email , <comment> a past email of yours , <comment> to make sure I 'm spelling your name correctly .
speaker: A
text: Yeah .
speaker: A
text: That 's good .
speaker: C
text: I worry about you .
speaker: A
text: I appreciate that .
speaker: B
text: But , when you abbreviate yourself as the " Basman " , you don't use any H 's .
speaker: A
text: " Basman " ?
speaker: A
text: Yeah , it 's because of the chessplayer named Michael Basman ,
speaker: A
text: who is my hero .
speaker: B
text: OK .
speaker: C
text: You 're a geek .
speaker: C
text: It 's O K .
speaker: C
text: I
speaker: C
text: How do you pronou
speaker: B
text: OK .
speaker: C
text: How do you pronounce your name ?
speaker: D
text: Eva .
speaker: C
text: Eva ?
speaker: D
text: Yeah .
speaker: A
text: Not Eva ?
speaker: C
text: What if I were <disfmarker> What if I were to call you Eva ?
speaker: D
text: I 'd probably still respond to it .
speaker: D
text: I 've had people call me Eva ,
speaker: D
text: but I don't know .
speaker: C
text: No , not just Eva , Eva .
speaker: C
text: Like if I u take the V and s pronounce it like it was a German V ?
speaker: B
text: Which is F .
speaker: C
text: Yeah .
speaker: D
text: Um , no idea then .
speaker: B
text: Voiced .
speaker: D
text: What ?
speaker: C
text: It sounds like an F .
speaker: D
text: I <disfmarker>
speaker: C
text: There 's also an F in German ,
speaker: D
text: OK .
speaker: C
text: which is why I <disfmarker>
speaker: B
text: Well , it 's just the difference between voiced and unvoiced .
speaker: C
text: Yeah .
speaker: D
text: OK .
speaker: C
text: As long as that 's O K .
speaker: D
text: Um .
speaker: C
text: I mean , I might slip out and say it accidentally .
speaker: C
text: That 's all I 'm saying .
speaker: D
text: That 's fine .
speaker: A
text: Yeah . It doesn't matter what those nodes are , anyway ,
speaker: A
text: because we 'll just make the weights " zero " for now .
speaker: B
text: Yep .
speaker: B
text: We 'll make them zero for now ,
speaker: B
text: because it <disfmarker> who <disfmarker> who knows what they come up with ,
speaker: B
text: what 's gonna come in there .
speaker: B
text: OK .
speaker: B
text: And , um ,
speaker: B
text: then
speaker: B
text: should we start on Thursday ?
speaker: A
text: OK .
speaker: B
text: And not meet tomorrow ?
speaker: A
text: Sure .
speaker: B
text: OK .
speaker: B
text: I 'll send an email ,
speaker: B
text: make a time suggestion .
speaker: C
text: Wait ,
speaker: C
text: maybe it 's OK , so that <disfmarker> that <disfmarker> that we can <disfmarker> that we have one node per construction .
speaker: C
text: Cuz even in people , like , they don't know what you 're talking about if you 're using some sort of strange construction .
speaker: B
text: Yeah , they would still c sort of get the closest , best fit .
speaker: C
text: Well , yeah , but I mean , the <disfmarker> uh , I mean , that 's what the construction parser would do .
speaker: C
text: Uh , I mean , if you said something completely arbitrary , it would f find the closest construction ,
speaker: B
text: Mm - hmm .
speaker: B
text: OK .
speaker: C
text: right ?
speaker: C
text: But if you said something that was completel er <disfmarker>
speaker: C
text: h theoretically the construction parser would do that <disfmarker>
speaker: C
text: But if you said something for which there was no construction whatsoever , n people wouldn't have any idea what you were talking about .
speaker: B
text: Mm - hmm .
speaker: C
text: Like " Bus dog fried egg . " I mean .
speaker: C
text: You know .
speaker: B
text: Or , if even something Chinese , for example .
speaker: C
text: Or , something in Mandarin , yeah .
speaker: C
text: Or Cantonese , as the case may be .
speaker: C
text: What do you think about that , Bhaskara ?
speaker: A
text: I mean <disfmarker>
speaker: A
text: Well <disfmarker>
speaker: A
text: But how many constructions do <disfmarker> could we possibly have <pause> nodes for ?
speaker: C
text: In this system , or in r
speaker: A
text: No , we .
speaker: A
text: Like , when people do this kind of thing .
speaker: C
text: Oh , when p How many constructions do people have ?
speaker: A
text: Yeah .
speaker: C
text: I have not <comment> the slightest idea .
speaker: A
text: Is it considered to be like in <disfmarker> are they considered to be like very , uh , sort of s abstract things ?
speaker: C
text: Every noun is a construction .
speaker: A
text: OK , so it 's like in the <pause> thousands .
speaker: C
text: The <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: Any <disfmarker> any form - meaning pair , to my understanding , is a construction .
speaker: A
text: OK .
speaker: B
text: So .
speaker: C
text: And form u starts at the level of noun <disfmarker> Or actually , maybe even sounds .
speaker: B
text: Phoneme .
speaker: B
text: Yep .
speaker: C
text: Yeah .
speaker: C
text: And goes upwards
speaker: C
text: until you get the ditransitive construction .
speaker: C
text: And then , of course , the c I guess , maybe there can be the <disfmarker>
speaker: A
text: S
speaker: C
text: Can there be combinations of the dit
speaker: A
text: Discourse - level <pause> constructions .
speaker: C
text: Yeah .
speaker: C
text: The " giving a speech " construction ,
speaker: B
text: Rhetorical constructions .
speaker: B
text: Yeah .
speaker: A
text: Yes .
speaker: B
text: But , I mean , you know , you can probably count <disfmarker> count the ways .
speaker: B
text: I mean .
speaker: C
text: It 's probab Yeah , I would s definitely say it 's finite .
speaker: B
text: Yeah .
speaker: C
text: And at least in compilers , that 's all that really matters ,
speaker: C
text: as long as your analysis is finite .
speaker: A
text: How 's that ? <nonvocalsound> How it can be finite , again ?
speaker: C
text: Nah , I can't think of a way it would be infinite .
speaker: B
text: Well , you can come up with new constructions .
speaker: C
text: Yeah . <comment> If the <disfmarker> if your <disfmarker> if your brain was totally non - deterministic , then perhaps there 's a way to get , uh , infin an infinite number of constructions that you 'd have to worry about .
speaker: A
text: But , I mean , in the <nonvocalsound> practical sense , it 's impossible .
speaker: C
text: Right . Cuz if we have a fixed number of neurons <disfmarker> ?
speaker: A
text: Yeah .
speaker: C
text: So the best - case scenario would be the number of constructions <disfmarker>
speaker: C
text: or , the worst - case scenario is the number of constructions equals the number of neurons .
speaker: A
text: Well , two to the power of the number of neurons .
speaker: C
text: Right .
speaker: C
text: But still finite .
speaker: B
text: OK .
speaker: C
text: No , wait .
speaker: C
text: Not necessarily , is it ?
speaker: C
text: We can end the <pause> meeting .
speaker: C
text: I just <disfmarker>
speaker: C
text: Can't you use different var different levels of activation ?
speaker: C
text: across , uh <disfmarker> lots of different neurons , to specify different values ?
speaker: B
text: Mm - hmm .
speaker: A
text: Um , yeah , but there 's , like , a certain level of <disfmarker>
speaker: C
text: There 's a bandwidth issue ,
speaker: C
text: right ?
speaker: A
text: Bandw - Yeah , so you can't do better than something .
speaker: C
text: Yeah .
speaker: B
text: Turn off the mikes .
speaker: B
text: Otherwise it gets really tough for the tr

Meeting summary:
text: The focus of the meeting was on a presentation of the work done already on the building of the Bayes-net.
text: The input layer deriving information from things like the user and situation models, feeds into a set of decision nodes, such as the Enter/View/Approach (EVA) endpoint.
text: In any particular situation, most of the outputs will not be relevant to the given context.
text: Therefore, they will either have to be pruned a posteriori, or only a subset of the possible decision nodes will be computed in each occasion.
text: The latter option could could follow a binary search-tree approach and it could also be better in computational terms.
text: In any case, on what basis the "winner" output is chosen is not clear.
text: One suggestion was discussed: the particular constructions used can determine the pertinent decision (output) nodes.
text: The complete prototype of the Bayes-net will be presented in the next meeting.
text: After that, it will be possible to define interfaces and a dummy construction parser, in order to test and link modules together.
text: The suggestion that the most appropriate decision node of the belief-net in each situation could be chosen as a function of what construction was used, was deemed unsuitable at this stage.
text: There are many interdependencies between the output nodes that this approach could not take into account.
text: The rest of the values for the Bayes-net nodes will be built in within the week.
text: The finished prototype will be presented during the next meeting.
text: Any set of inputs will provide either the whole range of output values of the Bayes-net or an a priori selection of those outputs.
text: In both cases, what is needed is a way to single out the appropriate outputs for any given context.
text: For example, in the case of a "where is" question, whether the prevalent output should be "Go-there" or "Info-on" or even a third option has to be computed somehow.
text: In any case, there are many input values that have not been entered in the Bayes-net at this stage.
text: Furthermore, no inputs for the Ontology and Discourse can be built in yet, as they involve research that will be carried out in the future.
text: Finally, there has been a problem with adding probabilities in a net created with the Hugin software, but this should be overcome very shortly.
text: The presented Bayes-net takes inputs from the Situation, User, Discourse and Ontology models.
text: There are several values (elements) defined in each of these models.
text: The inputs are fed into the belief-net, which, in turn, outputs the posterior probabilities for the values of all the decision nodes.
text: These comprise "Go-there", "EVA", "Info-on", "Location", "Timing", etc.
text: At this stage, all the decision nodes are evenly weighted: regardless of the context, each output is trusted equally.
text: Input and output node structure was presented in XML, as this is the format that will be used for the system.
text: A large number of the value probabilities have already been set.