grad b: Could I hit hit F - seven to do that ? on the Robert ? grad a: I 'm grad b: Oh , the remote will do it OK . grad b: Cuz I 'm already up there ? grad a: in control here . grad b: So , we were Ah ! grad c: Johno , where are you ? grad b: OK . grad c: Should you go back to the first one ? grad b: Do I wanna go back to the first one ? grad c: Well grad b: OK . grad d: I 'm sorry I grad c: Well , just to grad b: OK . It 's basically  talks about  It just refers to the fact that  one of main things we had to do was to decide what the intermediate sort of nodes were , grad d: I can read ! I 'm kidding . grad a: But if you really want to find out what it 's about you have to click on the little light bulb . grad b: Although I 've I 've never I don't know what the light bulb is for . grad a: Do you wanna try ? grad d: Ach u grad b: I 'd prefer not to . Is that the idea ? grad a: Why are you doing this in this mode and not in the presentation mode ? grad d: OK . grad b: Because I 'm gonna switch to the JavaBayes program grad a: Oh ! OK . grad b: You want me to Wait , what do you want me to do ? grad c: Can you maximize the window so all that stuff on the side isn't doesn't appear ? grad a: No , It 's OK . grad b: Well I can do that , but then I have to end the presentation in the middle so I can go back to open up grad c: OK , fine . grad b: So then the features we decided or we decided we were talked about , right ?  the the prosody , the discourse , verb choice . Whether the and this i we actually have a separate feature but I decided to put it on the same line for space . " Nice walls " which we can look up because  if you 're gonna get real close to a building in the Tango mode , right , there 's gotta be a reason for it . And it 's either because you 're in route to something else or you wanna look at the walls . The context , which in this case we 've limited to " business person " , " tourist " , or " unknown " , the time of day , and " open to suggestions " , isn't actually a feature . can I just ask the nice walls part of it is that  , in this particular domain you said be i it could be on two different lines but are you saying that in this particular domain it happens the that landmark - iness cor is correlated with grad b: Oh grad c: No . grad b: I either could put " nice walls " on its own line or " open to suggestions " off the slide . grad c: Like you could have a p grad d: And and By " nice " you mean grad c: You Like you could have a post office with  you know , nice murals or something . grad b: Or one time I was at this grad d: So " nice walls " is a stand in for like architecturally it ,  significant grad b: But see the thing is , if it 's grad c: Architecturally appealing from the outside . grad b: Yeah but if it 's architecturally significant you might be able to see it from Like you m might be able to " Vista " it , grad a:   grad b: Yeah , versus , like , I was at this place in Europe where they had little carvings of , like , dead people on the walls or something . grad b: But if you looked at it real close , you could see the the in intricacy of the of the walls . grad a: The grad d: Something you want to inspect at close range because it 's interesting . grad b: Robert ? grad a: Well there there is a term that 's often used . And I was just wondering whether that 's the same as what you describe as " landmark - iness " . There 's landmark for  , touristic reasons and landmark for I don't know navigational reasons or something . Tourist - y landmarks also happen to be Wouldn't couldn't they also be They 're not exclusive groups , are they ? Like non - tourist - y landmarks and grad a: Or it can be als grad b: direct navigational grad d: They 're not mutually exclusive ? grad b: Yeah . grad b: OK , So our initial idea was not very satisfying , because  our initial idea was basically all the features pointing to the output node . grad b: And  , so we Reasons being , you know , it 'd be a pain to set up all the probabilities for that . If we moved onto the next step and did learning of some sort ,  according Bhaskara we 'd be handicapped . grad c: Well usually , you know , N If you have N features , then it 's two to the N or exponential in N . So then our next idea was to add a middle layer , right ? So the thinking behind that was we have the features that we 've drawn from the communication of some Like , the someone s The person at the screen is trying to communicate some abstract idea , like " I 'm " the the abstract idea being " I am a tourist I want to go to this place . " Right ? So we 're gonna set up features along the lines of where they want to go and what they 've said previously and whatnot . Right ? but the middle thing , we were thinking along the lines of maybe trying to figure out , like , the concept of whether they 're a tourist or whether they 're running an errand or something like that along those lines . So then the hidden variables hair variables we came up with were whether someone was on a tour , running an errand , or whether they were in a hurry , because we were thinking  , if they were in a hurry there 'd be less likely to like or th grad c: Want to do Vista , grad b: Right . grad c: right ? Because if you want to view things you wouldn't be in a hurry . grad b: Or they might be more likely to be using the place that they want to go to as a like a navigational point to go to another place .  right now it 's still kind of in a toy version of it , because we didn't know the probabilities of or Well I 'll talk about it when I get the picture up . " Verb used " is actually personally amusing mainly because it 's it 's just whether the verb is a Tango verb , an Enter verb , or a Vista verb . grad c: Yeah , that one needs a lot of grad d: And are those mutually exclusive sets ? grad b: No . grad c: But  that would 've made the probably significantly be more complicated to enter , grad d: Got it . grad c: so we decided that for the purposes of this it 'd be simpler to just have three verbs .  Why don't you mention things about this , Bhaskara , that I am not that are not coming to my mind right now . grad c: OK , so Yeah , so note the four nodes down there , the sort of , the things that are not directly extracted . The " closed " is also not directly extracted I guess , from the  grad b: Well i it 's grad c:  . grad d: From the utterance ? grad b: it 's so it sort of is grad c: Actually , no , wait . grad b: because it 's because have the the time of day grad c: It is . grad c: Right , so f Right , but the other ones , the final destination , the whether they 're doing business , whether they 're in a hurry , and whether they 're tourists , that kind of thing is all  sort of you know probabilistically depends on the other things . So we haven't  , managed Like we don't have nodes for " discourse " and " parse " , although like in some sense they are parts of this belief - net . grad c: But  The idea is that we just extract those features from them , so we don't actually have a node for the entire parse , grad d:   grad d: So some of the the top row of things What 's what 's " Disc admission fee " ? grad c: whether they discuss the admission fees . So we looked at the data and in a lot of data people were saying things like " Can I get to this place ? " grad d: Oh . So that 's like a huge  clue that they 're trying to Enter the place rather than  to Tango or Vista , grad d:  - huh . grad b: There were there 'd be other things besides just the admission fee , but you know , we didn't have grad d:   So there are certain cues that are very strong either lexical or topic - based  , concept cues grad b: From the discourse that Yeah . And then in that second row or whatever that row of Time of Day through that So all of those Some of them come from the utterance and some of them are sort of either world knowledge or situational things . grad d: " Unmark @ @ Time of Day " grad c: Yeah , I m  grad a: Yeah . I would actually suggest we go through this one more time so we we all  , agree on what what the meaning of these things is at the moment and maybe what changes we grad b: Yeah , th OK . so one thing I I 'm you know unsure about , is how we have the discus  the " admission fee " thing set up . So one thing that we were thinking was by doing the layers like this ,  we kept  things from directly affecting the mode beyond the concept , but you could see perhaps discus the " admission fee " going directly to the mode pointing at " Enter " , grad a:   grad b: right ? Versus pointing to just at " tourist " , grad d:   grad b: But we just decided to keep all the things we extracted to point at the middle and then down . That 's because we 're talking about landmarks as touristic landmarks not as possible  grad b: Right . grad c: Disc - " admission fee " is a binary thing , " time of day " is like morning , afternoon , night . grad b: That 's how we have it currently set up , grad a: Yep . grad b: but it could be , you know , based upon hour grad c: Yeah . Normally context will include a huge amount of information , but  , we are just using the particular part of the context which consists of the switch that they flick to indicate whether they 're a tourist or not , I guess . grad c: So Right , grad d: Right ? grad c: so it 's not really all of context . Similarly prosody is not all of prosody but simply for our purposes whether or not they appear tense or relaxed . grad a: The the So the context is a switch between tourist or non - tourist ? grad c: and grad a: Or also unknown ? grad b: Or un unknown , grad a: OK . Unknown , right ? grad d: So final dest So it seems like that would really help you for doing business versus tourist , grad c: Which is th Which one ? grad d: but OK . so the the context being  , e I don't know if that question 's sort of in general , " are you "  the ar ar are do they allow business people to be doing non - business things at the moment ? grad c: Yeah , it does . So then you just have some probabilities over grad c: Everything is probablistic , and There 's always grad d: OK . " Verb used " is like , right now we only have three values , but in general they would be a probability distribution over all verbs . grad c:  " nice walls " is binary , " closed " is binary " final destination " , again Yeah , all those are binary I guess . grad c: Yeah , anything with a question mark after it in that picture is a binary node . Right ? grad c: Which things ? grad a: Nice walls ? grad b: Wi grad d:   grad b: It is binary but it doesn't have question mark because it 's extracted . grad a: So we can either be in a hurry or not , but we cannot be in a medium hurry at the moment ? grad c: Well , we To do that we would add another  value for that . grad c: And that would require s updating the probability distribution for " mode " as well . grad d: So  , of course this will happen when we think more about the kinds of verbs that are used in each cases grad a: Yeah , yeah . grad d: but you can imagine that it 's verb plus various other things that are also not in the bottom layer that would that would help you Like it 's a conjunction of , I don't know , you know , the verb used and some other stuff that that would determine grad c: Right . grad a: well the the sort of the landmark is is sort of the object right ? the argument in a sense ? grad d: Usually . I I don't know if that 's always the case I I guess haven't looked at the data as much as you guys have . grad a: that 's always warping on something some entity , grad d:   grad a: and   maybe at this stage we will we do want to  sort of get  modifiers in there grad b:  . grad a: because they may also tell us whether the person is in a hurry or not grad b: I want to get to the church quickly , grad c: Yeah . Do we have anything else to say about this ? grad c: We can do a little demo . grad a: No , then it wouldn't be a demo I was just gonna s grad c:  We can do a demo in the sense that we can  , just ob observe the fact that this will , in fact do inference . grad c: So we can , you know , set some of the  nodes and then try to find the probability of other nodes . grad c: just  I don't know , say they discussed the admission fee grad b: OK . grad c: and  the place has nice walls grad b: I love nice walls , OK ? I 'm a big fan . grad d: it 's starting to grow on me grad b: And the time of day is night ? grad c: Yeah , no wait . grad b: One thing that bugs me about JavaBayes is you have to click that and do this . grad c: So that is the probability that they 're Entering , Vista - ing or Tango - ing . grad c: And  grad d: So slightly biased toward " Tango " ing grad c: Yeah . grad b: If it 's night time , they have not discussed admission fee , and the n walls are nice . The reason I say the demo doesn't work very well is yesterday we  observed everything in favor of taking a tour , and it came up as " Tango " , right ? Over and over again . grad c: Like , we totally hand - tuned the probabilities , grad d: Yeah . We were like "  , well if the person does this and this and this , let 's say forty percent for this , grad d: OK . grad a: Yeah but it it grad d: Maybe the bias toward " Tango " ing was yours , then ? grad b: Yeah , grad c: Yeah . grad b: that 's that 's at grad c: It 's So we have to like fit the probabilities . grad d: So , the real case ? grad a: However you know , it The purpose was not really , at this stage , to come up with meaningful probabilities but to get thinking about that hidden middle layer . grad a: And grad b: We would actually I guess once we look at the data more we 'll get more hidden nodes , grad a:   grad b: No , I think we should have  exponentially more middle nodes than features we 've extracted . Whether you 're It 's whether It 's not grad d: And are th grad c: I think it 's more like " Are you are tourist ? are you in Ham - like Heidelberg for a " grad d: Oh , so , I thought that was directly given by the context switch . What if the context , which is not set , but still they say things like , " I want to go  , see the  the the castle and  , et cetera . " grad a: Is it grad b: Well the I kind of thought of " doing business " as more of running an errand type thing . grad a: So if you run out of cash as a tourist , and and and you need to go to the AT grad b: So i wi th grad d: OK . grad a: " How do I get to the bank ? " grad d: I see . grad c: And that 'll affect whether you want to enter or you if you kinda thing . grad c: Yeah , I think this context node is a bit of a I don't know , like in d  Do we wanna have Like it 's grad d: Are you assuming that or not ? Like is that to be  if that 's accurate then that would determine tourist node . grad c: If the context were to set one way or another , that like strongly   , says something about whether whether or not they 're tourists . grad c: So what 's interesting is when it 's not when it 's set to " unknown " . grad a: We - what set the they set the context to " unknown " ? grad d: OK . grad c: Right now we haven't observed it , so I guess it 's sort of averaging over all those three possibilities . grad a: And if we now do leave everything else as is the results should be the same , grad b: Oops . grad c: Well no , because we Th - the way we set the probabilities might not have Yeah , it 's it 's an it 's an issue , right ? Like grad a: Pretty much the same ? grad c: Yeah , it is . So the issue is that  in belief - nets , it 's not common to do what we did of like having , you know , a d bunch of values and then " unknown " as an actual value . What 's common is you just like don't observe the variable , grad d: Yeah . grad c: But  We didn't do this because we felt that there 'd I guess we were thinking in terms of a switch that actually grad b: We were thi Yeah , grad a:   grad b: We were th grad c: But  I don't know y what the right thing is to do for that . grad a: Why don't we Can we ,  How long would it take to to add another node on the observatory and , play around with it ? grad c: Another node on what ? grad b: well it depends on how many things it 's linked to . If we create something that for example would be  So th some things can be landmarks in your sense but they can never be entered ? So for example s a statue . grad a: So maybe we wanna have " landmark " meaning now " enterable landmark " versus ,  something that 's simply just a vista point , for example . grad a: Yeah ?  , a statue or  grad c: So basically it 's addressing a variable that 's " enterable or not " . grad b: Also you know , didn't we have a size as one ? The size of the landmark . grad c: What ? grad b: Cuz if it 's grad c:  . grad b: For some reason I had that OK , that was a thought that I had at one point but then went away . grad c: So you want to have a a node for like whether or not it can be entered ? grad a: Well , for example , if we include that , yeah ? grad c: Yeah . grad a: accessibility or something , yeah ? " Is it Can it be entered ? " grad c:  . In the sense that , you know , if it 's Tom the house of Tom Cruise , you know , it 's enterable but you may not enter it . grad a: Yeah ? and  And these are very observable sort of from the from the ontology sort of things . grad b: Way Does it actually help to distinguish between those two cases though ? Whether it 's practically speaking enterable , or actually physically enterable or not ? grad a: y y If If you 're running an errand you maybe more likely to be able to enter places that are usually not al w you 're not usually not allowed to  m grad d: It seems like it would for  ,  determining whether they wanna go into it or not . grad b: Well I can see why grad d: Cuz they grad a: Let 's get this  b clearer . grad b: Whether it 's a Whether it 's a public building , and whether it 's actually has a door . grad a: This is sort of  grad b: So Tom Cruise 's house is not a public building grad d:   grad b: OK , sh explain to me why it 's necessary to distinguish between whether something has a door and is not public . Or , if something It seems like it 's equivalent to say that it doesn't have a door a and it grad a:   grad b: Or " not public " and " not a door " are equivalent things , grad a: Yeah . So we would have What does it mean , then , that we have to we have an object type statue . grad a: And then we have , for example , an object type , that 's a hotel . It 's the hotel Zum Ritter , which is the only Renaissance building in Heidelberg that was left after the big destruction and for the Thirty Years War , blah - blah - blah .  - And lots of detail , c and carvings , engravings and so forth , grad b: Excellent . So I guess your question is so far I have no really arg no real argument why to differentiate between statues as statues and houses of celebrities , from that point of view . Let Let 's do a Can we add , just so I can see how it 's done , a " has door " property or ? grad b: OK . grad c: What would it , connect to ? Like , what would , it affect ? grad a: I think , it might affect Oh actually it 's it it wouldn't affect any of our nodes , right ? grad c: What I was thinking was if you had a like grad a: Oh it 's it affects th The " doing business " is certainly not . grad b: You could affect Theoretically you could affect " doing business " with " has door " . grad a: right ? grad c: Yeah , I don't know if JavaBayes is nice about that . It might be that if you add a new thing pointing to a variable , you just like it just overwrites everything . Whew ! grad c: Well that 's fine , but we have to see the function now . grad b: This grad c: What would be nice if it is if it just like kept the old function for either value but . grad b: Oh wait , it might be Did we w Yes , that 's not good . grad a: Maybe you can read in ? grad c: Ha - So have you used JavaBayes a lot ? grad d: Yes . Really I ha I 've I haven't used it a lot and I haven't used it in the last you know many months so grad c: OK . grad c: Like , we looked at sort of  a page that had like a bunch of grad d: Yeah . grad c:  in a way this is a lot of good features in Java it 's cra has a GUI and it 's  grad d:   grad c: What ? grad b: Maybe it did a little bit of learning , grad c: OK . grad a: What is the c code ? Can w can we see that ? How do you write the code grad b: The c grad a: or do you actually never have to write any code there ? grad c: Yeah . grad b: Oh man , grad c: Like , there 's the grad b: I didn't n Is there an ampersand in DOS ? grad c: Nope . grad c: It 'll ask you what you what it wants what you want to open it with and see what BAT , I guess . grad c: That 's Oh ! grad b: Maybe it was just grad a: Oh . grad b: I like I like Word Pad because it has the  the returns , grad a: Wordpad ? I grad b: the carriage returns on some of them . grad b: You know how they get " auto - fills " I guess , grad a: Mmm  grad b: It just basically looks like it just specifies a bunch of grad a:   grad b: It just that it 's grad c: But they 're not very friendly . grad b: Yeah the ordering isn't very clear on grad c: So you 'd have to like figure out Like you have to go and grad d: Right . grad c: it 's not grad b: We were doing it grad c: Yeah we can maybe write an interface th for  entering probability distributions easily , something like like a little script . I actually seem to recall Srini complaining about something to do with Entering probability so this is probably grad c: The other thing is it is in Java grad d: Yeah , it 's Yeah . grad b: Or grad a: Do you have the true source files or just the class ? grad b: I don't know if he actually grad c: Yeah . we do grad b: Does he grad c: I I saw directory called " source " , grad b: Oh . grad c: I think it might it might be simpler to just have a script that , you know It 's , like , friendly , grad d: The d the data tables . grad a: But if th if there is an XML file that or format that it can also read  it just reads this , right ? When it starts . grad b: Yeah I know there is an I was looking on the we web page and he 's updated it for an XML version of I guess Bayes - nets . grad c: The JavaBayes guy ? So but , e he doesn't use it . So in what sense has he updated it ? grad b: Well th you can either you ca or you can read both . grad b: Because  Well at least the  I could have misread the web page , I have a habit of doing that , but . grad a: So you got more slides ? grad b: Do I have more slides ?  yes , one more . E That 's maybe , I don't know If grad b:  that 's future future work . grad b: And of course if you have a presentation that doesn't have something that doesn't work at all , then you have " What I learned " , as a slide . grad b: I know what I like about these meetings is one person will nod , and then the next person will nod , and then it just goes all the way around the room . So this means  grad b: Should I pull up the net again ? grad d: Yeah . grad d: So a more general thing than " discussed admission fee "  , could be I I 'm just wondering whether the context , the background context of the discourse might be I don't know , if there 's a way to define it or maybe you know generalize it some way  , there might be other cues that , say , in the last few utterances there has been something that has strongly associated with say one of the particular modes  , I don't know if that might be grad a:   I think we grad d: and and into that node would be various various things that that could have specifically come up . grad a: I think a a sort of general strategy here You know , this is this is excellent because  it gets you thinking along these terms is that maybe we ob we could observe a couple of  discourse phenomena such as the admission fee , and something else and something else , that happened in the discourse before . And maybe there are two  So maybe this could be sort of a separate region of the net , which has two has it 's own middle layer . Maybe this , you know , has some kind of  , funky thing that di if this and this may influence these hidden nodes of the discourse which is maybe something that is  , a more general version of the actual phenomenon that you can observe . So things that point towards grad b: So instead of single node , for like , if they said the word " admission fee " grad d: Exactly . grad b: " admission fee " , or maybe , you know , " how much to enter " grad d: Yeah . Yeah ? And then maybe there are some discourse acts if they happened before ,  it 's more for  a cue that the person actually wants to get somewhere else and that you are in a in a in a route  , sort of proceeding past these things , so this would be just something that where you want to pass it .  ? Is that it ? However these are of course then the the nodes , the observed nodes , for your middle layer . So this again points to " final destination " , " doing business " , " tourist hurry " and so forth . we have a whole region " in a e grad d: That 's a whole set of discourse related cues to your middle layer . grad d: Right ? grad a: So e because at the end the more we  add , you know , the more spider - web - ish it 's going to become in the middle and the more of hand editing . They ra may have there own hidden layer that points to some of the the real hidden layer ,  or the general hidden layer . grad a: And the same we will be able to do for syntactic information , the verbs used , the object types used , modifiers . grad b: One thing that 's kind of been bugging me when I more I look at this is that the I guess , the fact that the there 's a complete separation between the observed features and in the output . grad b: For instance if the discourse does grad d: What do you mean by that ? grad b: well for instance , the " discourse admission fee " node seems like it should point directly to the grad d:  - huh . grad b: or increase the probability of " enter directly " versus " going there via tourist " . Like we could add a node like do they want to enter it , which is affected by admission fee and by whether it 's closed and by whether it has a door . And if it if you do it If you could connect it too hard you may get such phenomenon that like " So how much has it cost to enter ? " and the answer is two hundred fifty dollars , and then the persons says  " Yeah I want to see it . " Yeah ? meaning " It 's way out of my budget "  grad b: There are places in Germany where it costs two hundred fifty dollars to enter ? grad a: nothing comes to mind . But i you know , i we can Something Somebody can have discussed the admission fee and u the answer is s if we  , you know ,  still , based on that result is never going to enter that building . So the discourse refers to " admission fee " but it just turns out that they change their mind in the middle of the discourse . you have to have some notion of not just  there 's a there 's change across several turns of discourse grad b: Right . grad d: so I don't know how if any of this was discussed but how i if it all this is going to interact with whatever general  , other other discourse processing that might be happen . grad b: What sort of discourse processing is  are the How much is built into SmartKom and grad a: It works like this . The first thing we get is that already the intention is sort of t They tried to figure out the intention , right ? simply by parsing it . And this  m won't differentiate between all modes , yeah ? but at least it 'll tell us " OK here we have something that somebody that wants to go someplace , now it 's up for us to figure out what kind of going there is is is happening , and  , if the discourse takes a couple of turns before everything all the information is needed , what happens is you know the parser parses it and then it 's handed on to the discourse history which is ,  o one of the most elaborate elaborate modules . It 's it 's actually the the whole memory of the entire system , that knows what wh who said what , which was what was presented . It helps an an anaphora resolution and it and it fills in all the structures that are omitted , so , because you say " OK , how can I get to the castle ? " Oh , how how much is it ? " and  " yeah I would like   to g let 's do it " and so forth . So even without an a ana anaphora somebody has to make sure that information we had earlier on is still here . so whenever the  ,  person is not actually rejecting what happened before , so as in " No I really don't want to see that movie . I 'd rather stay home and watch TV "  What movie was selected in what cinema in what town is is going to be sort of added into the disc into the representations every di at each dialogue step , by the discourse model discourse model , Yeah , that 's what it 's called . and , it does some help in the anaphora resolution and it also helps in coordinating the gesture screen issues . So a person pointing to something on the screen , you know , the discourse model actually stores what was presented at what location on the s on the screen grad b:  . grad a: so it 's a it 's a rather huge huge thing but   we can sort of It has a very clear interface . We can query it whether admission fees were discussed in the last turn and and the turn before that or you know how deep we want to search grad b: OK . How deep do we want to sear , you know ?  but we should try to keep in mind that , you know , we 're doing this sort of for research , so we we should find a limit that 's reasonable and not go , you know , all the way back to Adam and Eve . You know , did that person ever discuss admissions fee fees in his entire life ? And the dialogues are pretty pretty you know concise and Anyway . grad d: So one thing that might be helpful which is implicit in the use of " admission fee discussion " as a cue for entry , is thinking about the plans that various people might have . This person is  , finding out information about this thing in order to go in as a tourist or finding out how to get to this place in order to do business .  , because then anything that 's a cue for one of the steps would be slight evidence for that overall plan . They 're in in non in sort of more traditional AI kinds of plan recognition things you sort of have you know , some idea at each turn of agent doing something , " OK , wha what plans is this a consistent with ? " and then get s some more information and then you see " here 's a sequence that this sort of roughly fits into " . grad d: I I don't know how you know you 'd have to figure out what knowl what knowledge representation would work for that . grad a: You know ? and it it 's fifty steps , grad d:   grad a: huh ? just for buying a ticket at a ticket counter , you know , and and maybe that 's helpful to look at it to look at those . W when we talked  we had the example , you know , of you being  a s a person on a ticket counter working at railway station and somebody r runs up to you with a suitcase in his hands , says New York and you say Track seven , huh ? And it 's because you know that that person actually is following , you know You execute a whole plan of going through a hundred and fifty steps , you know , without any information other than " New York " , huh ? inferring everything from the context .  , even though there is probably no train from here to New York , right ? grad d: Mmm . Right ? Is that t San Francisco , Chicago ? grad b: I think grad a: Is that possible ? grad b: One time I saw a report on trains , and I think there is a l I don't know if I thought there was a line that went from somewhere , maybe it was Sacramento to Chicago , grad a:   grad d: The Transcontinental Railroad , doesn't that ring a bell ? grad b: Yeah but I don't know if it 's still grad d: I think it has to exist somewhere . grad a: Well it never went all the way , right ?  you always had to change trains at Omaha , grad d: Well most of the way . grad a: right ? One track ended there and the other one started at five meters away from that grad d:  . grad a: yeah ? Has anybody ever been on an Amtrak ? grad d: I have . grad c: What ? Why ? grad b: I just They seem to have a lot of accidents on the Amtrak . grad a: But you know , I don't know whether it 's which ones are safer , you know , statistically . Yeah , they 're Yeah , it 's way better grad a: yeah I used  Amtrak quite a bit on the east coast and I was surprised . grad a:  ? grad c: I I want to see what it does with  " landmark - iness " . grad d: So by the way tha that structure that Robert drew on the board was like more  , cue - type - based , right , here 's like we 're gonna segment off a bit of stuff that comes from discourse and then some of the things we 're talking about here are more you know , we mentioned maybe if they talk about  , I don't know , entering or som you know like they might be more task - based . grad d: So I I don't know if there There 's obviously some m more than one way of organizing the variables into something grad a: I think that  What you guys did is really nicely sketching out different tasks , and maybe some of their conditions . grad a: One task is more likely you 're in a hurry when you do that kind of s doing business , grad d:   grad a: and and less in a hurry when  you 're a tourist  tourists may have never have final destinations , you know because they are eternally traveling around so maybe what what what happened what might happen is that we do get this sort of task - based middle layer , grad d:   grad a: and then we 'll get these sub - middle layers , that are more cue - based . So ,  I suggest w to for to proceed with this in in the sense that maybe throughout this week the three of us will will talk some more about maybe segmenting off different regions , and we make up some some toy a observable " nodes " is that what th grad b: Refined y re just refine the grad a: What 's the technical term ? grad c: OK . For which ? grad a: For the  nodes that are observable ? The " outer layer " ? grad c: Just observable nodes , grad b: The features , grad c: evidence nodes ? grad b: I don't know , whatever you grad a: Feature ma make up some features for those Identify four regions , grad c: Yeah . grad a: maybe make up some features for each region and  and  ,  and  middle layer for those . And then these should then connect somehow to the more plan - based deep space grad c: Yeah . grad c: Yeah , this is totally like The probabilities and all are completely ad - hoc .  but , they 're even like  like , close to the end we were like , you know we were like  really ad - hoc . grad c: Right ? Cuz if it 's like ,  If it 's four things coming in , right ? And , say , some of them have like three possibilities and all that . So you 're thinking like like a hundred and forty four or something possible things numbers to enter , grad d: And That 's terrible . grad b: Some of them are completely absurd too , like they want to enter , but it 's closed , grad d: That 's  Well grad b: it 's night time , you know there are tourists and all this weird stuff happens at the line up and you 're like grad c: Yeah , the only like possible interpretation is that they are like come here just to rob the museum or something to that effect . grad d: In which case you 're supposed to alert the authorities , and see appropriate action . Yeah , another thing to do , is also to ,  I guess to ask around people about other Bayes - net packages . grad d: Sorry , Wednesday , grad b: Who 's talking on Wednesday ? grad c: Maybe we can ask him about it . grad b: I haven't J Jerry never sent out a sent out an email , did he , ever ? grad c: No . grad d: Ben ? grad a: Ben , then , grad d: I think it 's Ben actually , grad a: Ben . I actually  , have  , also we can  , start looking at the SmartKom tables and I will grad b: Right . grad b: Do you want to trade ? grad a: no I I actually made a mistake because it it fell asleep and when Linux falls asleep on my machine it 's it doesn't wake up ever , so I had to reboot grad d: Oh , no . grad a: And if I reboot without a network , I will not be able to start SmartKom , because I need to have a network . grad b:  grad a: So we 'll do that t maybe  grad c: But . But once you start sart start SmartKom you can be on You don't have to be on a network anymore . grad b: Why does SmartKom need a network ? grad a:  it looks up some stuff that , you know , is is that is in the written by the operating system only if it if you get a DHCP request , so it you know , my computer does not know its IP address , you know ? grad b: Ah . grad a: And I don't have an IP address , they can't look up they don't know who localhost is , and so forth and so forth . grad a: She 's willing to do it , meaning be the wizard for the data collection , also maybe transcribe a little bit , if she has to , but also recruiting subjects , organizing them , and so forth . Jerry however suggested that we should  have a trial run with her , see whether she can actually do all the  spontaneous , eloquent and creativeness that we  expect of the wizard . And I talked to Liz about this and it looks as if Friday afternoon will be the time when we have a first trial run for the data . grad c: So who would be the subject of this trial run ? grad a: Pardon me ? grad c: Who Will there be a Is one Is you one of you gonna be the subject ? Like are you grad a:  Liz also volunteered to be the first subject , which I think might be even better than us guys . grad a: If we do need her for the technical stuff , then of course one of you has to sort of  jump in . grad c: Well I just figured it has to be someone who 's , familiar enough with the data to cause problems for the wizard , so we can , see if they 're you know good .  that 's what we wanna check , right ? grad a: grad d: Well , in this case it 's a p it 's a sort of testing of the wizard rather than of the subject . grad c: Isn't that what it is ? grad d: It 's  grad a: yes w we we would like to test the wizard , but you know , if we take a subject that is completely unfamiliar with the task , or any of the set up , we get a more realistic grad c: I guess that would be reasonable . grad d: I 'm sure if we  , You think there 's a chance we might need Liz for , whatever , the technical side of things ? I 'm sure we can get other people around who don't know anything  , if we want another subject . So , is it a experimental setup for the  , data collection totally ready determined ? grad b: I like that . grad a:  I think it 's it 's it 's  experimental setup u on the technical issue yes , except we st I think we still need  a recording device for the wizard , just a tape recorder that 's running in a room . grad a: But  in terms of specifying the scenario ,    we 've gotten a little further grad d:   grad a: but  we wanted to wait until we know who is the wizard , and have the wizard partake in the ultimate sort of definition probe . So so if if on Friday it turns out that she really likes it and and we really like her , then nothing should stop us from sitting down next week and getting all the details completely figured out . So the ideal task  , will have whatever I don't know how much the structure of the evolving Bayes - net will af affect Like we wanna we wanna be able to collect as much of the variables that are needed for that , grad a: Mmm - yea - some . grad d: right ? in the course of the task ? Well not all of them but you know . grad a: Bu - e e e I 'm even This this Tango , Enter , Vista is sort of , itself , an ad - hoc scenario . So we wanted just to collect data , to get that that that elicits more , that elicits richer language . grad a: And we actually did not want to constrain it too much , grad d:   And then maybe we 'll discover the phenomenon the phenomena that we want to solve , you know , with whatever engine we we come up with . So this this this is a parallel track , you know , there they hopefully meet , grad d: OK . grad a: but since grad d: It could it could be used for not just this task . grad a: It should tell us , you know , what kind of phenomenon could occur , it should tell us also maybe something about the difference between people who think they speak to a computer versus people who think they speak to a human being grad d:   So it may get us some more information on the human - machine pragmatics , that no one knows anything about , as of yesterday . And secondly , now that of course we have sort of started to lick blood with this , and especially since  Johno can't stop Tango - ing , we may actually include , you know , those those intentions . So now I think we should maybe have at least one navigational task with with sort of explicit  grad d:   grad a: not ex it 's implicit that the person wants to enter , grad d:   grad a: and maybe some task where it 's more or less explicit that the person wants to take a picture , grad d:   grad a: Whereas , you know , if we 'd just get data we 'd never know what they actually wanted , we 'd get no cues . grad b: So is this the official end of the meeting now ? grad c: Yep . grad c: So what 's " Economics , the fallacy " ? grad a: Ma grad b: I just randomly label things . grad c: Oh , really ? grad a: Maybe we ought to switch off these things before we continue