Grad B: Mm - hmm .
Grad C: wouldn't it ? Here 's a k Oh ! Oh , I 'll wait until you 're plugged in . Oh , don't sit there . Sit here . You know how you don't like that one . It 's OK . That 's the weird one . That 's the one that 's painful . That hurts . It hurts so bad . I 'm h I 'm happy that they 're recording that . That headphone . The headphone that you have to put on backwards , with the little little thing and the little little foam block on it ? It 's a painful , painful microphone .
Grad B: I think it 's th called " the Crown " .
Grad C: The crown ?
Grad D: What ?
Grad B: Yeah , versus " the Sony " .
Grad A: The Crown ? Is that the actual name ? OK .
Grad B: Mm - hmm . The manufacturer .
Grad C: I don't see a manufacturer on it .
Grad B: You w
Grad C: Oh , wait , here it is . h This thingy . Yeah , it 's " The Crown " . The crown of pain !
Grad A: Yes .
Grad B: You 're on - line ?
Grad C: Are you are your mike o Is your mike on ?
Grad A: Indeed .
Grad C: OK . So you 've been working with these guys ? You know what 's going on ?
Grad A: Yes , I have . And , I do . Yeah , alright . s So where are we ?
Grad C: Excellent !
Grad B: We 're discussing this .
Grad A: I don't think it can handle French , but anyway .
Grad B: So . Assume we have something coming in . A person says , " Where is X ? " , and we get a certain We have a Situation vector and a User vector and everything is fine ? An - an and and our and our Grad C: Did you just sti Did you just stick the m the the the microphone actually in the tea ?
Grad A: No .
Grad B: And , um ,
Grad A: I 'm not drinking tea . What are you talking about ?
Grad C: Oh , yeah . Sorry .
Grad B: let 's just assume our Bayes - net just has three decision nodes for the time being . These three , he wants to know something about it , he wants to know where it is , he wants to go there .
Grad C: In terms of , these would be wha how we would answer the question Where - Is , right ? We u This is i That 's what you s it seemed like , explained it to me earlier
Grad B: Yeah , but , mmm .
Grad C: w We we 're we wanna know how to answer the question " Where is X ? "
Grad B: Yeah . No , I can I can do the Timing node in here , too , and say " OK . "
Grad C: 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 . We just want to know how we should answer " Where is X ? "
Grad B: OK . And , um , OK , and , Go - there has two values , right ? , Go - there and not - Go - there . Let 's assume those are the posterior probabilities of that .
Grad A: Mm - hmm .
Grad B: Info - on has True or False and Location . So , he wants to know something about it , and he wants to know something he wants to know Where - it - is ,
Grad A: Excuse me .
Grad B: has these values . And , um ,
Grad C: Oh , I see why we can't do that .
Grad B: And , um , in this case we would probably all agree that he wants to go there . Our belief - net thinks he wants to go there ,
Grad A: Yeah .
Grad B: right ?
Grad A: Mm - hmm .
Grad B: In the , uh , whatever , if we have something like this here , and this like that and maybe here also some Grad A: You should probably {comment} make them out of Yeah .
Grad B: something like that ,
Grad C: Well , it
Grad B: 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 there . " Right ?
Grad C: That it Doesn't this assume , though , that they 're evenly weighted ?
Grad D: True .
Grad C: Like I guess they are evenly weighted .
Grad A: The different decision nodes , you mean ?
Grad C: Yeah , the Go - there , the Info - on , and the Location ?
Grad A: Well , d yeah , this is making the assumption . Yes .
Grad C: Like Grad B: What do you mean by " differently weighted " ? They don't feed into anything really anymore .
Grad A: But I mean , why do we Grad C: Or I jus
Grad A: 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 .
Grad C: Le
Grad A: So , in that sense , we weight them equally right now .
Grad B: OK . Makes sense . Yeah . But Grad C: So the But I guess the k the question that I was as er wondering or maybe Robert was proposing to me is How do we d make the decision on as to which one to listen to ?
Grad A: Yeah , so , the final d decision is the combination of these three . So again , it 's it 's some kind of , uh Grad C: Bayes - net .
Grad A: Yeah , sure .
Grad C: OK so , then , the question i So then my question is t to you then , would be 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 ? Cuz oth If we 're just taking arbitrary language in , we couldn't have a node for every possible question , you know ?
Grad A: A decision node for every possible question , you mean ?
Grad C: Well , I like , in the case of Yeah . In the ca Any piece of language , we wouldn't be able to answer it with this system , b if we just h Cuz we wouldn't have the correct node . Basically , w what you 're s proposing is a n Where - Is node , right ?
Grad A: Yeah .
Grad C: And and if we And if someone says , you know , uh , something in Mandarin to the system , we 'd - wouldn't know which node to look at to answer that question ,
Grad A: So is Yeah . Yeah .
Grad C: right ?
Grad B: Mmm ?
Grad C: So , but but if we have a finite What ?
Grad B: I don't see your point . What what 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 . And now we need an expert system or belief - net or something that interprets that , that looks at all the values and says , " The winner is Timing . Now , go there . " " Uh , go there , Timing , now . " Or , " The winner is Info - on , Function - Off . " So , he wants to know something about it , and what it does . Nuh ? Uh , regardless of of of the input . Wh - Regardle
Grad C: Yeah , but But how does the expert but how does the expert system know how who which one to declare the winner , if it doesn't know the question it is , and how that question should be answered ?
Grad B: 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 .
Grad C: Yeah I know . But how do we weight what we get out ? As , which one i Which ones are important ? So my i So , if we were to it with a Bayes - net , we 'd have to have a node for every question that we knew how to deal with , that would take all of the inputs and weight them appropriately for that question .
Grad B: Mm - hmm .
Grad C: Does that make sense ? Yay , nay ?
Grad A: 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 ?
Grad C: We Grad A: Is that your point ?
Grad C: Well , no . I I guess my question is , Is the reason that we can make a node f or OK . So , lemme see if I 'm confused . Are we going to make a node for every question ? Does that make sense ? Grad A: For every question ?
Grad C: Or not .
Grad A: Like Grad C: Every construction .
Grad A: Hmm . I don't Not necessarily , I would think . I mean , it 's not based on constructions , 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 .
Grad C: Wel W OK . So , someone asked a question .
Grad A: Yeah .
Grad C: How do we decide how to answer it ?
Grad B: Well , look at look Face yourself with this pr question . You get this You 'll have y This is what you get . And now you have to make a decision . What do we think ? What does this tell us ? And not knowing what was asked , and what happened , and whether the person was a tourist or a local , because all of these factors have presumably already gone into making these posterior probabilities . What what we need is a just a mechanism that says , " Aha ! There is "
Grad C: Yeah . I just don't think a " winner - take - all " type of thing is the Grad A: I mean , in general , like , we won't just have those three , right ? We 'll have , uh , like , many , many nodes . So we have to , like So that it 's no longer possible to just look at the nodes themselves and figure out what the person is trying to say .
Grad B: Yep . Because there are interdependencies , right ? The uh Uh , no . So if if for example , the Go - there posterior possibility is so high , um , uh , w if it 's if it has reached reached a certain height , then all of this becomes irrelevant . So . If even if if the function or the history or something is scoring pretty good on the true node , true value Grad C: Wel I don't know about that , cuz that would suggest that I mean Grad B: He wants to go there and know something about it ?
Grad C: Do they have to be mutual Yeah . Do they have to be mutually exclusive ?
Grad B: I think to some extent they are . Or maybe they 're not .
Grad C: Cuz I , uh The way you describe what they meant , they weren't mutu uh , they didn't seem mutually exclusive to me .
Grad B: Well , if he doesn't want to go there , even if the Enter posterior proba So .
Grad C: Wel
Grad B: Go - there is No . Enter is High , and Info - on is High .
Grad C: Well , yeah , just out of the other three , though , that you had in the Grad B: Hmm ?
Grad C: those three nodes . The - d They didn't seem like they were mutually exclusive .
Grad B: No , there 's No . But It 's through the Grad C: So th s so , yeah , but some So , some things would drop out , and some things would still be important .
Grad B: Mm - hmm .
Grad C: 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 ,
Grad A: Mm - hmm .
Grad C: you know , uh , is the only reason OK , so , I guess , if we have a Ba - another Bayes - net to deal with this stuff , the only r reason we can design it is cuz we know what each question is asking ?
Grad A: Yeah . I think that 's true .
Grad C: And then , so , the only reason way we would know what question he 's asking is based upon Oh , so if Let 's say I had a construction parser , and I plug this in , I would know what each construction the communicative intent of the construction was
Grad A: Mm - hmm .
Grad C: and so then I would know how to weight the nodes appropriately , in response . So no matter what they said , if I could map it onto a Where - Is construction , I could say , " ah !
Grad A: Ge Mm - hmm .
Grad C: well the the intent , here , was Where - Is " ,
Grad A: OK , right .
Grad C: and I could look at those .
Grad A: Yeah . Yes , I mean . Sure . You do need to know I mean , to have that kind of information .
Grad B: Hmm . Yeah , I 'm also agreeing that a simple pru {comment} Take the ones where we have a clear winner . Forget about the ones where it 's all sort of middle ground . Prune those out and just hand over the ones where we have a winner . Yeah , because that would be the easiest way . We just compose as an output an XML mes message that says . " Go there now . " " Enter historical information . " And not care whether that 's consistent with anything . Right ? But in this case if we say , " definitely he doesn't want to go there . He just wants to know where it is . " or let 's call this this " Look - At - H " He wants to know something about the history of . So he said , " Tell me something about the history of that . " Now , the e But for some reason the Endpoint - Approach gets a really high score , too . 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 . Right ? Somebody needs to zap that . You know ? Or know There needs to be some knowledge that Grad C: We Yeah , but , the Bayes - net that would merge I just realized that I had my hand in between my mouth and my micr er , my and my microphone . 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 , and based upon that node , then you would look at the other stuff .
Grad B: Yep . Yep .
Grad C: I mean , it i Does that make sense ?
Grad B: Yep . It 's sort of one of those , that 's It 's more like a decision tree , if if you want . You first look o at the lowball ones ,
Grad C: Yeah , i
Grad B: and then Grad C: Yeah , I didn't intend to say that every possible OK . There was a confusion there , k I didn't intend to say every possible thing should go into the Bayes - net , because some of the things aren't relevant in the Bayes - net for a specific question . 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 .
Grad B: Mm - hmm .
Grad A: Right .
Grad C: Show us the way , Bhaskara .
Grad A: I guess the other thing is that um , yeah . I mean , when you 're asked a specific question and you don't even Like , if you 're asked a Where - Is question , you may not even look like , ask for the posterior probability of the , uh , EVA node , right ? Cuz , that 's what I mean , in the Bayes - net you always ask for the posterior probability of a specific node . So , I mean , you may not even bother to compute things you don't need .
Grad B: Um . Aren't we always computing all ?
Grad A: No . You can compute , uh , the posterior probability of one subset of the nodes , given some other nodes , but totally ignore some other nodes , also . Basically , things you ignore get marginalized over .
Grad B: Yeah , but that 's that 's just shifting the problem . Then you would have to make a decision ,
Grad A: Yeah . So you have to make Grad B: " OK , if it 's a Where - Is question , which decision nodes do I query ? "
Grad A: Yeah . Yes . But I would think that 's what you want to do .
Grad B: That 's un
Grad A: Right ?
Grad B: Mmm .
Grad D: Well , eventually , you still have to pick out which ones you look at .
Grad B: Yeah .
Grad D: So it 's pretty much the same problem ,
Grad B: Yeah it 's it 's it 's apples and oranges .
Grad D: isn't it ?
Grad B: Nuh ? I mean , maybe it does make a difference in terms of performance , computational time .
Grad A: Mm - hmm .
Grad B: 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 ,
Grad A: Mmm .
Grad B: or you just make a p @ @ {comment} a priori estimate of what you think might be relevant and query those .
Grad A: Yeah .
Grad C: So basically , you 'd have a decision tree query , Go - there . If k if that 's false , query this one . If that 's true , query that one . And just basically do a binary search through the ?
Grad A: I don't know if it would necessarily be that , uh , complicated . But , uh I mean , it w
Grad C: Well , in the case of Go - there , it would be . In the case Cuz if you needed an If y If Go - there was true , you 'd wanna know what endpoint was . And if it was false , you 'd wanna d look at either Lo - Income Info - on or History .
Grad A: Yeah . That 's true , I guess . Yeah , so , in a way you would have that .
Grad C: Also , I 'm somewhat boggled by that Hugin software .
Grad A: OK , why 's that ?
Grad C: I can't figure out how to get the probabilities into it . Like , I 'd look at Grad A: Mm - hmm .