input: What was discussed about contextualizing output?
  Grad B: So I guess this is more or less now just to get you up to date, Johno. This is what, uh,
  Grad C: This is a meeting for me.
  Grad B: um, Eva, Bhaskara, and I did.
  Grad D: Did you add more stuff to it? {pause} later?
  Grad B: Um. Why?
  Grad D: Um. I don't know. There were, like, the {disfmarker} you know, @ @ and all that stuff. But. I thought you {disfmarker} you said you were adding stuff
  Grad B: Uh, no.
  Grad D: but {pause} I don't know.
  Grad B: This is {disfmarker} Um, Ha! Very nice. Um, so we thought that, {vocalsound} We can write up uh, an element, and {disfmarker} for each of the situation nodes that we observed in the Bayes - net? So. What's the situation like at the entity that is mentioned? if we know anything about it? Is it under construction? Or is it on fire or something {pause} happening to it? Or is it stable? and so forth, going all the way um, f through Parking, Location, Hotel, Car, Restroom, @ @ {comment} Riots, Fairs, Strikes, or Disasters.
  Grad C: So is {disfmarker} This is {disfmarker} A situation are {disfmarker} is all the things which can be happening right now? Or, what is the situation type?
  Grad B: That's basically {pause} just specifying the {disfmarker} the input for the {disfmarker} w what's
  Grad C: Oh, I see y Why are you specifying it in XML?
  Grad B: Um. Just because it forces us to be specific about the values {pause} here?
  Grad C: OK.
  Grad B: And, also, I mean, this is a {disfmarker} what the input is going to be. Right? So, we will, uh {disfmarker} This is a schema. This is {disfmarker}
  Grad C: 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?
  Grad B: 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. Right? 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." So that's gonna be {disfmarker} {vocalsound} {comment} Um.
  Grad C: So this is the situational context, everything in it. Is that what Situation is short for, shi situational context?
  Grad B: Yep.
  Grad C: OK.
  Grad B: So this is just, again, a an XML schemata which defines a set of possible, uh, permissible XML structures, which we view as input into the Bayes - net. Right?
  Grad C: And then we can r {pause} uh possibly run one of them uh transformations? That put it into the format that the Bayes n or Java Bayes or whatever wants?
  Grad B: Yea - Are you talking {disfmarker} are you talking about the {disfmarker} the structure?
  Grad C: Well it {disfmarker}
  Grad B: I mean when you observe a node.
  Grad C: When you {disfmarker} when you say {pause} the input to the {pause} v Java Bayes, {comment} it takes a certain format,
  Grad B: Um - hmm.
  Grad C: right? Which I don't think is this. Although I don't know.
  Grad B: No, it's certainly not this. Nuh.
  Grad C: So you could just {disfmarker} Couldn't you just run a {disfmarker}
  Grad B: XSL. {comment} Yeah.
  Grad C: Yeah. To convert it into the Java Bayes for format?
  Grad B: Yep.
  Grad C: OK.
  Grad B: That's {disfmarker} That's no problem, but I even think that, um {disfmarker} I mean, once {disfmarker} Once you have this sort of as {disfmarker} running as a module {disfmarker} Right? What you want is {disfmarker} You wanna say," OK, give me the posterior probabilities of the Go - there {pause} node, when this is happening." Right? When the person said this, the car is there, it's raining, and this is happening. And with this you can specify the {disfmarker} what's happening in the situation, and what's happening with the user. So we get {disfmarker} After we are done, through the Situation we get the User Vector. So, this is a {disfmarker}
  Grad C: So this is just a specification of all the possible inputs?
  Grad B: Yep. And, all the possible outputs, too. So, we have, um, for example, the, uh, Go - there decision node
  Grad C: OK.
  Grad B: which has two elements, going - there and its posterior probability, and not - going - there and its posterior probability, 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.
  Grad C: And then we would just look at the, eh, Struct that we wanna look at in terms of if {disfmarker} if we're only asking about one of the {disfmarker} 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?
  Grad B: Um, pretty much, yes, but I think it's a little bit more complex. As, if I understand it correctly, it always gives you all the posterior probabilities for all the values of all decision nodes. So, when we input something, we always get the, uh, posterior probabilities for all of these. Right?
  Grad C: OK.
  Grad B: So there is no way of telling it t not to tell us about the EVA {pause} values.
  Grad C: Yeah, wait I agree, that's {disfmarker} yeah, use {disfmarker} oh, uh {pause} Yeah, OK.
  Grad B: So {disfmarker} so we get this whole list of {disfmarker} of, um, things, and the question is what to do with it, what to hand on, how to interpret it, in a sense. 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, look which one {disfmarker}
  Grad C: Look at that Struct in the output,
  Grad B: Yep.
  Grad C: right?
  Grad B: Look at that Struct in the {disfmarker} the output, even though I wouldn't call it a" Struct" . But.
  Grad C: Well i well, it's an XML Structure that's being res returned,
  Grad B: Oh. Mm - hmm.
  Grad C: right?
  Grad B: So every part of a structure is a" Struct" . Yeah.
  Grad C: Yeah, I just uh {disfmarker} I just was {disfmarker} abbreviated it to Struct in my head, and started going with that.
  Grad B: That element or object, I would say.
  Grad C: Not a C Struct. That's not what I was trying to k
  Grad B: Yeah.
  Grad C: though yeah.
  Grad B: OK. And, um, 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} Um. So. The, uh {disfmarker} Let's look at an example.
  Grad 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?
  Grad B: Yeah. w We'd need to prune. Right? Throw things away.
  Grad C: Well, actually, you don't even need to do that with XML.
  Grad B: No
  Grad C: D Can't you just look at one specific {disfmarker}
  Grad B: Yeah, exactly. The {disfmarker} @ @ {comment} Xerxes allows you to say, u" Just give me the value of that, and that, and that." 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. So the person said, um," Where is X?" and so, we want to know, um, is {disfmarker} Does he want info? o on this? or know the location? Or does he want to go there? Let's assume this is our {disfmarker} our question.
  Grad C: Sure.
  Grad B: Nuh? So. Um. Do this in Perl. So we get {disfmarker} OK. Let's assume this is the output. So. We should con be able to conclude from that that {disfmarker} 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, or how likely it is that he wants to get information. But, maybe w we should just reverse this to make it a little bit more delicate. So, does he wanna know where it is? or does he wanna go there?
  Grad C: He wants to know where it is.
  Grad B: Right. I {disfmarker} I {disfmarker} I tend to agree. And if it's {disfmarker} If {disfmarker}
  Grad C: Well now, y I mean, you could {disfmarker}
  Grad B: And i if there's sort of a clear winner here, and, um {disfmarker} and this is pretty, uh {disfmarker} indifferent, then we {disfmarker} then we might conclude that he actually wants to just know where, uh t uh, he does want to go there.
  Grad C: Uh, out of curiosity, is there a reason why we wouldn't combine these three nodes? into one smaller subnet? that would just basically be {pause} the question for {disfmarker} We have" where is X?" is the question, right? That would just be Info - on or Location? Based upon {disfmarker}
  Grad B: Or Go - there. A lot of people ask that, if they actually just wanna go there. People come up to you on campus and say," Where's the library?" You're gonna say {disfmarker} y you're gonna say, g" Go down that way." 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}" Grad C: 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.
  Grad B: Um. I don't know whether I understand what you mean. 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 the nicest way, use a cab, or so s 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. 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. And some {disfmarker} some things will always be sort of too {disfmarker} not significant enough.
  Grad C: Wha Or i or i it'll be tight. You won't {disfmarker} it'll be hard to decide.
  Grad B: Yep.
  Grad C: 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, which makes me think Bayes - net should be the way to solve these things. So if you had {disfmarker} If for every construction,
  Grad B: Oh!
  Grad C: right? you could say," Well, there {disfmarker} Here's the Where - Is construction." And for the Where - Is construction, we know we need to l look at this node, that merges these three things together
  Grad B: Mm - hmm.
  Grad C: as for th to decide the response. And since we have a finite number of constructions that we can deal with, we could have a finite number of nodes.
  Grad B: OK. Mm - hmm.
  Grad C: Say, if we had to y deal with arbitrary language, it wouldn't make any sense to do that, because there'd be no way to generate the nodes for every possible sentence.
  Grad B: Mm - hmm.
  Grad C: But since we can only deal with a finite amount of stuff {disfmarker}
  Grad B: So, basically, the idea is to f to feed the output of that belief - net into another belief - net.
  Grad C: Yeah, so basically take these three things and then put them into another belief - net.
  Grad B: But, why {disfmarker} why {disfmarker} why only those three? Why not the whol
  Grad C: Well, I mean, d For the Where - Is question. So we'd have a node for the Where - Is question.
  Grad B: Yeah. But we believe that all the decision nodes are {disfmarker} can be relevant for the Where - Is, and the Where {disfmarker} How - do - I - get - to or the Tell - me - something - about.
  Grad C: You can come in if you want.
  Grad B: Yes, it is allowed.
  Grad C: As long as y you're not wearing your h your h headphones. Well, I do I {disfmarker} See, I don't know if this is a {pause} good idea or not. I'm just throwing it out. 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
  Grad B: Mm - hmm. Yep.
  Grad C: node thing stuff. And uh {disfmarker}
  Grad D: OK.
  Grad B: I mean {disfmarker} Let's not forget we're gonna get some very strong {pause} input from {pause} these sub dis from these discourse things, right? So." Tell me the location of X." Nuh? Or" Where is X located at?" Grad C: We u
  Grad B: Nuh?
  Grad C: 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,
  Grad B: Mm - hmm.
  Grad C: wouldn't it? Here's a k Oh! Oh, I'll wait until you're {pause} 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 {pause} that you have to put on backwards, with the little {disfmarker} little thing {disfmarker} and the little {disfmarker} 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 {disfmarker} 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 {disfmarker} We have a Situation vector and a User vector and everything is fine? An - an and {disfmarker} and our {disfmarker} and our {disfmarker}
  Grad C: Did you just sti Did you just stick the m the {disfmarker} the {disfmarker} 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 {disfmarker} i That's what you s it seemed like, explained it to me earlier
  Grad B: Yeah, but, mmm.
  Grad C: w We {disfmarker} we're {disfmarker} we wanna know how to answer the question" Where is X?" Grad B: Yeah. No, I can {disfmarker} 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 {disfmarker} 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 {disfmarker}
  Grad A: You should probably {comment} make them out of {disfmarker} 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 {pause} there." Right?
  Grad C: That it {disfmarker} Doesn't this assume, though, that they're evenly weighted?
  Grad D: True.
  Grad C: Like {disfmarker} 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 {disfmarker}
  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 {disfmarker}
  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 {disfmarker}
  Grad C: 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} How do we d make the decision on {disfmarker} as to {disfmarker} which one to listen to?
  Grad A: Yeah, so, the final d decision is the combination of these three. So again, it's {disfmarker} it's some kind of, uh {disfmarker}
  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 {disfmarker} 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 {disfmarker} like, in the case of {disfmarker} 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 {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,
  Grad A: So is {disfmarker} Yeah. Yeah.
  Grad C: right?
  Grad B: Mmm?
  Grad C: So, but {disfmarker} but if we have a finite {disfmarker} What?
  Grad B: I don't see your point. 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. 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 {pause} something about it, and what it does. Nuh? Uh, regardless of {disfmarker} of {disfmarker} of the input. Wh - Regardle
  Grad C: 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?
  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 {disfmarker} 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 {disfmarker}
  Grad A: Is that your point?
  Grad C: Well, no. I {disfmarker} I guess my question is, Is the reason that we can make a node f or {disfmarker} OK. So, lemme see if I'm confused. Are we going to make a node for every question? Does that make sense? {disfmarker}
  Grad A: For every question?
  Grad C: Or not.
  Grad A: Like {disfmarker}
  Grad C: Every construction.
  Grad A: Hmm. I don't {disfmarker} 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 {disfmarker} look {disfmarker} Face yourself with this pr question. You get this {disfmarker} You'll have {disfmarker} 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 {disfmarker} what we need is a {disfmarker} just a mechanism that says," Aha! There is {disfmarker}" Grad C: Yeah. I just don't think a" winner - take - all" type of thing is the {disfmarker}
  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 {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.
  Grad B: Yep. Because there are interdependencies, right? The uh {disfmarker} Uh, no. So if {disfmarker} if for example, the Go - there posterior possibility is so high, um, uh, w if it's {disfmarker} if it has reached {disfmarker} reached a certain height, then all of this becomes irrelevant. 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}
  Grad C: Wel I don't know about that, cuz that would suggest that {disfmarker} I mean {disfmarker}
  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 {disfmarker} 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 {disfmarker}
  Grad B: Hmm?
  Grad C: those three nodes. The - d They didn't seem like they were mutually exclusive.
  Grad B: No, there's {disfmarker} No. But {disfmarker} It's through the {disfmarker}
  Grad C: So th s so, yeah, but some {disfmarker} 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 {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?
  Grad A: Yeah. I think that's true.
  Grad C: And then, so, the only reason {disfmarker} way we would know what question he's asking is based upon {disfmarker} 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
  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 {disfmarker} I mean, to have that kind of information.
  Grad B: Hmm. Yeah, I'm also agreeing that {pause} 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 {vocalsound} message that says." Go there {pause} 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 {disfmarker} 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, {pause} 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 {disfmarker} There needs to be some knowledge that {disfmarker}
  Grad C: We {disfmarker} Yeah, but, the Bayes - net that would merge {disfmarker} 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 {disfmarker} It's more like a decision tree, if {disfmarker} if you want. You first look o at the lowball ones,
  Grad C: Yeah, i
  Grad B: and then {disfmarker}
  Grad C: Yeah, I didn't intend to say that every possible {disfmarker} 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 {disfmarker} 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, right? Cuz, that's what {disfmarker} 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 {disfmarker} that's just shifting the problem. Then you would have to make a decision,
  Grad A: Yeah. So you have to make {disfmarker}
  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 {disfmarker} it's {disfmarker} it's {disfmarker} 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 {pause} query, {pause} 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 {disfmarker}?
  Grad A: I don't know if it would necessarily be that, uh, complicated. But, uh {disfmarker} I mean, it w
  Grad C: Well, in the case of Go - there, it would be. In the case {disfmarker} 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, {vocalsound} 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 {disfmarker}
  Grad A: Mm - hmm.
  Grad C: It's somewha It's boggling me.
  Grad A: OK. Alright. Well, hopefully it's {pause} fixable.
  Grad C: Ju
  Grad A: It's {disfmarker} there's a {disfmarker}
  Grad C: 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.
  Grad A: OK.
  Grad B: Um, by the way, are {disfmarker} Do we know whether Jerry and Nancy are coming?
  Grad A: So we can figure this out.
  Grad B: Or {disfmarker}?
  Grad A: They should come when they're done their stuff, basically, whenever that is. So.
  Grad C: What d what do they need to do left?
  Grad A: Um, I guess, Jerry needs to enter marks, but I don't know if he's gonna do that now or later. But, uh, if he's gonna enter marks, it's gonna take him awhile, I guess, and he won't be here.
  Grad C: And what's Nancy doing?
  Grad A: Nancy? Um, she was sorta finishing up the, uh, calculation of marks and assigning of grades, but I don't know if she should be here. Well {disfmarker} or, she should be free after that, so {disfmarker} assuming she's coming to this meeting. I don't know if she knows about it.
  Grad C: She's on the email list, right?
  Grad A: Is she? OK.
  Grad B: Mm - hmm. OK. Because 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
  Grad D: OK.
  Grad B: sometime {pause} this week {pause} again
  Grad A: OK.
  Grad B: and finish up the, uh, values of this. So we have, uh {disfmarker} Believe it or not, we have all the bottom ones here.
  Grad C: Well, I {disfmarker}
  Grad D: You added a bunch of {pause} nodes, for {disfmarker}?
  Grad B: Yep. We {disfmarker} we {disfmarker} we have {disfmarker} Actually what we have is this line.
  Grad D: OK.
  Grad B: Right?
  Grad C: Uh, what do the, uh, structures do?
  Grad B: Hmm?
  Grad C: So the {disfmarker} the {disfmarker} the {disfmarker} For instance, this Location node's got two inputs,
  Grad A: Four inputs.
  Grad B: Hmm.
  Grad C: that one you {disfmarker}
  Grad B: Four.
  Grad A: Those are {disfmarker} The bottom things are inputs, also.
  Grad C: Oh, I see.
  Grad A: Yeah.
  Grad C: OK, that was OK. That makes a lot more sense to me now.
  Grad B: Yep.
  Grad C: Cuz I thought it was like, that one in Stuart's book about, you know, the {disfmarker}
  Grad A: Alarm in the dog?
  Grad C: U Yeah.
  Grad A: Yeah.
  Grad C: Or the earthquake and the alarm.
  Grad A: Sorry. Yeah, I'm confusing two.
  Grad C: Yeah, there's a dog one, too, but that's in Java Bayes,
  Grad A: Right.
  Grad C: isn't it?
  Grad A: Maybe.
  Grad C: But there's something about bowel problems or something with the dog.
  Grad A: Yeah.
  Grad B: And we have all the top ones, all the ones to which no arrows are pointing. What we're missing are the {disfmarker} these, where arrows are pointing, where we're combining top ones. So, we have to come up with values for this, and this, this, this, and so forth. And maybe just fiddle around with it a little bit more. And, um. And then it's just, uh, edges, many of edges. And, um, we won't {comment} meet next Monday. So.
  Grad C: Cuz of Memorial Day?
  Grad A: We'll meet next Tuesday, I guess.
  Grad B: Yep. Yeah.
  Grad C: When's Jerry leaving for {disfmarker} Italia?
  Grad B: On {disfmarker} on Friday.
  Grad A: Which Friday?
  Grad B: This {disfmarker} this Friday.
  Grad A: OK.
  Grad D: Oh. This Friday?
  Grad C: Ugh.
  Grad B: This Friday.
  Grad C: As in, four days?
  Grad B: Yep.
  Grad C: Or, three days?
  Grad A: Is he {disfmarker} How long is he gone for?
  Grad B: Two weeks.
  Grad A: Italy, huh? What's, uh {disfmarker} what's there?
  Grad B: Well, it's a country. Buildings. People.
  Grad A: Pasta.
  Grad C: But it's not a conference or anything.
  Grad B: Hmm?
  Grad C: He's just visiting.
  Grad A: Right. Just visiting.
  Grad B: Vacation.
  Grad A: It's a pretty nice place, in my brief, uh, encounter with it.
  Grad B: Do you guys {disfmarker} 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. Um, so {disfmarker}
  Grad C: Oh, I think we should disappoint him.
  Grad B: Yeah? You {disfmarker} or have a finished construction parser and a working belief - net, and uh {disfmarker}
  Grad C: That wouldn't be disappointing. I think w we should do absolutely no work for the two weeks that he's gone.
  Grad B: Well, that's actually what I had planned, personally. 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. And then, I sort of {disfmarker}
  Grad C: Oh, yeah, that sounds good, too.
  Grad B: sort of bask in {disfmarker} in your glory. But, uh, i do you guys have any vacation plans, because I myself am going to be, um, gone, but this is actually not really important. Just this weekend we're going camping.
  Grad C: Yeah, I'm wanna be this {disfmarker} gone this weekend, too.
  Grad B: Ah. But we're all going to be here on Tuesday again? Looks like it?
  Grad D: Yeah.
  Grad B: OK, then. Let's meet {disfmarker} meet again next Tuesday. And, um, finish up this Bayes - net. And once we have finished it, I guess we can, um {disfmarker} and that's going to be more just you and me, because Bhaskara is doing probabilistic, recursive, structured, object - oriented, uh,
  Grad C: Killing machines!
  Grad B: reasoning machines.
  Grad A: Yes.
  Grad B: And, um {disfmarker}
  Grad C: Killing, reasoning. What's the difference?
  Grad D: Wait. So you're saying, next Tuesday, is it the whole group meeting, or just us three working on it, or {disfmarker} or {disfmarker}?
  Grad B: Uh. The whole group. And we present our results, our final,
  Grad D: OK.
  Grad B: definite {disfmarker}
  Grad D: So, when you were saying we {pause} need to do a re - run of, like {disfmarker}
  Grad A: h What?
  Grad D: What {disfmarker} Like, just working out the rest of the {disfmarker}
  Grad B: Yeah. We should do this th the upcoming days.
  Grad D: This week?
  Grad B: So, this week, yeah.
  Grad C: When you say," the whole group" , you mean {pause} the four of us, and Keith?
  Grad D: OK.
  Grad B: And, Ami might.
  Grad C: Ami might be here, and it's possible that Nancy'll be here?
  Grad B: Yep.
  Grad C: So, yeah.
  Grad B: Because, th you know, once we have the belief - net done {disfmarker}
  Grad C: You're just gonna have to explain it to me, then, on Tuesday, how it's all gonna work out. You know.
  Grad B: We will. OK. Because then, once we have it sort of up and running, then we can start you know, defining the interfaces and then feed stuff into it and get stuff out of it, and then hook it up to some fake construction parser and {disfmarker}
  Grad C: That you will have in about nine months or so.
  Grad B: Yeah.
  Grad C: Yeah.
  Grad B: And, um,
  Grad C: The first bad version'll be done in nine months.
  Grad B: Yeah, I can worry about the ontology interface and you can {disfmarker} Keith can worry about the discourse. 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, right?
  Grad A: Which {disfmarker}
  Grad B: where the {disfmarker}
  Grad A: Which ones?
  Grad B: 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. Right?
  Grad A: Um, what do you mean? If the Go - there says No, then the Go - there is {disfmarker}
  Grad D: I don't get it.
  Grad A: I don't u understand.
  Grad B: Um.
  Grad A: Like, the Go - there depends on all those four things.
  Grad B: Yep.
  Grad A: Yeah.
  Grad B: But, what are the values of the Go - there - discourse?
  Grad A: Well, it depends on the situation. If the discourse is strongly indicating that {disfmarker}
  Grad B: Yeah, but, uh, we have no discourse input.
  Grad A: Oh, I see. The d See, uh, specifically in our situation, D and O are gonna be, uh {disfmarker} Yeah. Sure. So, whatever.
  Grad D: So, so far we have {disfmarker} Is that what the Keith node is?
  Grad B: Yep.
  Grad D: OK. And you're taking it out? {pause} for now?
  Grad B: Well, this is D {disfmarker}
  Grad D: Or {disfmarker}?
  Grad B: OK, this, I can {disfmarker} I can get it in here.
  Grad D: All the D's are {disfmarker}
  Grad B: I can get it in here, so th We have the, uh, um, sk let's {disfmarker} let's call it" Keith - Johno
  Grad A: Johno?
  Grad B: node" . There is an H {comment} somewhere printed.
  Grad C: There you go.
  Grad A: Yeah. People have the same problem with my name.
  Grad B: Yeah.
  Grad A: Oops.
  Grad B: And, um,
  Grad C: Does th th does the H go b before the A or after the A?
  Grad A: Oh, in my name? Before the A.
  Grad C: Yeah. OK, good. Cuz you kn When you said people have the same problem, I thought {disfmarker} Cuz my H goes after the uh e e e the v
  Grad A: People have the inverse problem with my name.
  Grad C: OK. 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.
  Grad A: Yeah. That's good.
  Grad C: I worry about you.
  Grad A: I appreciate that.
  Grad B: But, when you abbreviate yourself as the" Basman" , you don't use any H's.
  Grad A:" Basman" ? Yeah, it's because of the chessplayer named Michael Basman, who is my hero.
  Grad B: OK.
  Grad C: You're a geek. It's O K. I
  Grad B: OK.
  Grad C: How do you pronou How do you pronounce your name?
  Grad D: Eva.
  Grad C: Eva?
  Grad A: Not Eva?
  Grad D: Yeah.
  Grad C: What if I were {disfmarker} What if I were to call you Eva?
  Grad D: I'd probably still respond to it. I've had people call me Eva, but I don't know.
  Grad C: No, not just Eva, Eva. Like if I u take the V and s pronounce it like it was a German V?
  Grad B: Which is F.
  Grad C: Yeah.
  Grad D: Um, no idea then.
  Grad B: Voiced.
  Grad D: What?
  Grad C: It sounds like an F.
  Grad D: I {disfmarker}
  Grad C: There's also an F in German,
  Grad D: OK.
  Grad B: Well, it's just the difference between voiced and unvoiced.
  Grad C: which is why I {disfmarker} Yeah.
  Grad D: OK.
  Grad C: As long as that's O K.
  Grad D: Um.
  Grad C: I mean, I might slip out and say it accidentally. That's all I'm saying.
  Grad D: That's fine.
  Grad A: Yeah. It doesn't matter what those nodes are, anyway, because we'll just make the weights" zero" for now.
  Grad B: Yep. We'll make them zero for now, because it {disfmarker} who {disfmarker} who knows what they come up with, what's gonna come in there. OK. And, um, then should we start on Thursday?
  Grad A: OK.
  Grad B: And not meet tomorrow?
  Grad A: Sure.
  Grad B: OK. I'll send an email, make a time suggestion.
  Grad C: Wait, maybe it's OK, so that {disfmarker} that {disfmarker} that we can {disfmarker} that we have one node per construction. Cuz even in people, like, they don't know what you're talking about if you're using some sort of strange construction.
  Grad B: Yeah, they would still c sort of get the closest, best fit.
  Grad C: Well, yeah, but I mean, the {disfmarker} uh, I mean, that's what the construction parser would do.
  Grad B: Mm - hmm.
  Grad C: Uh, I mean, if you said something completely arbitrary, it would f find the closest construction,
  Grad B: OK.
  Grad C: right? But if you said something that was completel er {disfmarker} h theoretically the construction parser would do that {disfmarker} But if you said something for which there was no construction whatsoever, n people wouldn't have any idea what you were talking about.
  Grad B: Mm - hmm.
  Grad C: Like" Bus dog fried egg." I mean. You know.
  Grad B: Or, if even something Chinese, for example.
  Grad C: Or, something in Mandarin, yeah. Or Cantonese, as the case may be. What do you think about that, Bhaskara?
  Grad A: I mean {disfmarker} Well {disfmarker} But how many constructions do {disfmarker} could we possibly have {pause} nodes for?
  Grad C: In this system, or in r
  Grad A: No, we. Like, when people do this kind of thing.
  Grad C: Oh, when p How many constructions do people have?
  Grad A: Yeah.
  Grad C: I have not {comment} the slightest idea.
  Grad A: Is it considered to be like in {disfmarker} are they considered to be like very, uh, sort of s abstract things?
  Grad C: Every noun is a construction.
  Grad A: OK, so it's like in the {pause} thousands.
  Grad C: The {disfmarker} Yeah. Any {disfmarker} any form - meaning pair, to my understanding, is a construction.
  Grad A: OK.
  Grad B: So.
  Grad C: And form u starts at the level of noun {disfmarker} Or actually, maybe even sounds.
  Grad B: Phoneme. Yep.
  Grad C: Yeah. And goes upwards until you get the ditransitive construction.
  Grad A: S
  Grad C: And then, of course, the c I guess, maybe there can be the {disfmarker} Can there be combinations of the dit
  Grad A: Discourse - level {pause} constructions.
  Grad C: Yeah. The" giving a speech" construction,
  Grad B: Rhetorical constructions.
  Grad A: Yes.
  Grad B: Yeah. But, I mean, you know, you can probably count {disfmarker} count the ways. I mean.
  Grad C: It's probab Yeah, I would s definitely say it's finite.
  Grad B: Yeah.
  Grad C: And at least in compilers, that's all that really matters, as long as your analysis is finite.
  Grad A: How's that? {nonvocalsound} How it can be finite, again?
  Grad C: Nah, I can't think of a way it would be infinite.
  Grad B: Well, you can come up with new constructions.
  Grad C: 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.
  Grad A: But, I mean, in the {nonvocalsound} practical sense, it's impossible.
  Grad C: Right. Cuz if we have a fixed number of neurons {disfmarker}?
  Grad A: Yeah.
  Grad C: So the best - case scenario would be the number of constructions {disfmarker} or, the worst - case scenario is the number of constructions equals the number of neurons.
  Grad A: Well, two to the power of the number of neurons.
  Grad C: Right. But still finite.
  Grad B: OK.
  Grad C: No, wait. Not necessarily, is it? We can end the {pause} meeting. I just {disfmarker} Can't you use different var different levels of activation? across, uh {disfmarker} lots of different neurons, to specify different values?
  Grad B: Mm - hmm.
  Grad A: Um, yeah, but there's, like, a certain level of {disfmarker}
  Grad C: There's a bandwidth issue,
  Grad A: Bandw - Yeah, so you can't do better than something.
  Grad C: right? Yeah.
  Grad B: Turn off the mikes. Otherwise it gets really tough for the tr
output: Due to most outputs not being relevant to the given context, they will either have to be pruned a posteriori, or only a subset of the possible decision nodes will be computed in each occasion. The latter option could follow a binary search-tree approach and it could also be better in computational terms. In any case, on what basis the" winner" output is chosen is not clear.