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


grad d: I tried to go for the EE Cummings sort of feeling , but grad a: Three three six zero zero . grad a: You ever seen " So I married an axe murderer " ? grad c:  parts of it . grad a: There 's a part wh there 's parts when he 's doing beat poetry . is when he 's he works in a coffee shop , in San Francisco , and  he 's sitting there on this couch and they bring him this massive cup of espresso , and he 's like " excuse me I ordered the large espresso ? " grad d:  . grad a: Wait do are y So you 're trying to decide who 's the best taster of tiramisu ? grad d: No ?  . There was a a a fierce argument that broke out over whose tiramisu might be the best and so we decided to have a contest where those people who claim to make good tiramisu make them , grad a: Ah . grad d: and then we got a panel of impartial judges that will taste do a blind taste and then vote . grad a: Seems like Seems like you could put a s magic special ingredient in , so that everyone know which one was yours . Well , I was thinking if  y you guys have plans for Sunday ? We 're we 're not it 's probably going to be this Sunday , but  we 're sort of working with the weather here because we also want to combine it with some barbecue activity where we just fire it up and what whoever brings whatever you know , can throw it on there . grad a: Well , I 'm going back to visit my parents this weekend , so , I 'll be out of town . grad d: So you 're going to the west Bay then ? No , grad a: No , the South Bay , grad d: south Bay ? grad a: yeah . Wonder if these things ever emit a very , like , piercing screech right in your ear ? grad d: They are gonna get more comfortable headsets . I actually  , even though Liz was  kind enough to offer to be the first subject , I sort of felt that she knew too much , so I asked  Litonya . grad d: So , this is what she saw as part of as  for instr introduction , this is what she had to read aloud .  , that was really difficult for her and  grad c: Because of l all the names , you mean ? grad d: The names and  this was the  first three tasks she had to to master after she called the system , and  then of course the system broke down , and those were the l   I should say the system was supposed to break down and then  these were the remaining three tasks that she was going to solve , with a human  . And now comes the This is the phone - in phase of grad c: Wait , can I I have a question . So there 's no system , right ? Like , there was a wizard for both  both parts , is this right ? grad d: Yeah . grad d: One time , pretending to be a system , one time , to pretending to be a human , which is actually not pretending . Isn't this kind of obvious when it says " OK now you 're talking to a human " and then the human has the same voice ? grad d: No no no . And  the wizard sometimes will not be audible , Because she was actually they there was some  lapse in the  wireless , we have to move her closer . grad a: Is she mispronouncing " Anlage " ? Is it " Anlaga " or " Anlunga " grad d: They 're mispronouncing everything , grad a: OK . Well , if we we  professor b: So , are are you trying to record this meeting ? grad d: There was a strange reflex .  , that was already anticipated by some people suggested that if we just have bullets here , they 're gonna not they 're subjects are probably not gonna going to follow the order . professor b: S so if you just number them " one " , " two " , " three " it 's grad d: Yeah , and make it sort of clear in the  professor b: OK . That is something that Fey actually thought of a in the last second that sh the system should introduce itself , when it 's called . grad d: And  , another suggestion , by Liz , was that we  , through subjects , switch the tasks . So when when they have task - one with the computer , the next person should have task - one with a human , and so forth .  , we have to refine the tasks more and more , which of course we haven't done at all , so far , in order to avoid this rephrasing , so where , even though w we don't tell the person " ask blah - blah - blah - blah - blah " they still try , or at least Litonya tried to  repeat as much of that text as possible . grad d: And  my suggestion is of course we we keep the wizard , because I think she did a wonderful job , professor b: Great . grad d: in the sense that she responded quite nicely to things that were not asked for , " How much is a t a bus ticket and a transfer " so this is gonna happen all the time , we d you can never be sure . grad a: I wasn't wasn't sure whether wizard was the correct term for  " not a man " . grad c: There 's no female equivalent of grad d: But  grad a: Are you sure ? grad c: No , I don't know . grad d: Well , there is witch and warlock , grad a: Yeah , that 's so @ @ . grad c: Yeah , that 's what I was thinking , but grad d: and  professor b: Right . And  So , some some work needs to be done , but I think we can  And this , and in case no you hadn't seen it , this is what Litonya looked at during the   while taking the while partaking in the data collection .  , do you know exactly how to do that , or is  Lila  , you know what exactly do we do to to put her on the payroll in some way ? grad d: I 'm completely clueless , but I 'm willing to learn . So anyway ,  grad d: N professor b: So why don't you  ask Lila and see what she says about you know exactly what we do for someone in th grad d: Student - type worker , professor b: Well , yeah she 's un she 's not a a student , grad d: or ? professor b: she just graduated but anyway . professor b: So i if Yeah , I agree , she sounded fine , she a actually was  , more  , present and stuff than than she was in conversation , so she did a better job than I would have guessed from just talking to her . grad d: This is sort of what I gave her , so this is for example h how to get to the student prison , professor b: Yeah . grad d: and I didn't even spell it out here and in some cases I I spelled it out a little bit  more thoroughly , professor b: Right . grad d: this is the information on on the low sunken castle , and the amphitheater that never came up , and  , so i if we give her even more  , instruments to work with I think the results are gonna be even better . professor b: Oh , yeah , and then of course as she does it she 'll she 'll learn @ @ .  And also if she 's willing to take on the job of organizing all those subjects and stuff that would be wonderful . professor b: And ,  she 's actually she 's going to graduate school in a kind of an experimental paradigm , so I think this is all just fine in terms of h her learning things she 's gonna need to know  , to do her career . professor b: So , I my guess is she 'll be r r quite happy to take on that job . grad d: And  I told her that we gonna  figure out a meeting time in the near future to refine the tasks and s look for the potential sources to find people . She also agrees that you know if it 's all just gonna be students the data is gonna be less valuable because of that so . professor b: Well , as I say there is this s set of people next door , it 's not hard to grad d: We 're already Yeah . professor b:  grad d: However , we may run into a problem with a reading task there . We could talk to the people who run it and  see if they have a way that they could easily  tell people that there 's a task , pays ten bucks or something , grad d:   Now , I signed us up for the Wednesday slot , and part of what we should do is this . professor b: So , my idea on that was  , partly we 'll talk about system stuff for the computer scientists , but partly I did want it to get the linguists involved in some of this issue about what the task is and all  you know , what the dialogue is , and what 's going on linguistically , because to the extent that we can get them contributing , that will be good . professor b: maybe we can get some of the linguists sufficiently interested that they 'll help us with it , other linguists , if you 're a linguist , but in any case , grad d: Yep . So my idea on on Wednesday is partly to  you  , what you did today would i is just fine . You just  do " this is what we did , and here 's the thing , and here 's s some of the dialogue and and so forth . " But then , the other thing of course is we should  give the computer scientists some idea of of what 's going on with the system design , and where we think the belief - nets fit in and where the pieces are and stuff like that . So , I don't I don't think it 's worth a lot of work , particularly on your part , to to to make a big presentation . I don't think you should you don't have to make any new  PowerPoint or anything . The  other two things is  we 've can have Johno tell us a little about this professor b: Great . grad d: and we also have a l little bit on the interface , M - three - L enhancement , and then  that was it , I think . grad a: So , what I did for this this is  , a pedagogical belief - net because I was I I took I tried to conceptually do what you were talking about with the nodes that you could expand out so what I did was I took I made these dummy nodes called Trajector - In and Trajector - Out that would isolate the things related to the trajector . And then I  did similar things for our our net to  with the context and the discourse and whatnot , so we could sort of isolate them or whatever in terms of the the top layer . Let 's go Slide all the way up so we see what the p the p very bottom looks like , or is that it ? grad a: Yeah , there 's just one more node and it says " Mode " which is the decision between the grad d: Yeah . grad a: So basically all I did was I took the last belief - net professor b: So   grad a: and I grouped things according to what how I thought they would fit in to  image schemas that would be related . And the two that I came up with were Trajector - landmark and then Source - path - goal as initial ones . grad a: And then I said well ,  the trajector would be the person in this case probably . grad a: you know , we have we have the concept of what their intention was , whether they were trying to tour or do business or whatever , professor b: Right . And then  in terms of the source , the things  the only things that we had on there I believe were whether Oh actually , I kind of , I might have added these cuz I don't think we talked too much about the source in the old one but  whether the where I 'm currently at is a landmark might have a bearing on whether grad d:   And " usefulness " is basi basically means is that an institutional facility like a town hall or something like that that 's not something that you 'd visit for tourist 's tourism 's sake or whatever . " Travel constraints " would be something like you know , maybe they said they can they only wanna take a bus or something like that , right ? And then those are somewhat related to the path , professor b:   grad a: so that would determine whether we 'd could take we would be telling them to go to the bus stop or versus walking there directly . Similar things as the source except they also added whether the entity was closed and whether they have somehow marked that is was the final destination .  , and then if you go up , Robert , Yeah , so  , in terms of Context , what we had currently said was whether they were a businessman or a tourist of some other person .  , Discourse was related to whether they had asked about open hours or whether they asked about where the entrance was or the admission fee , or something along those lines . grad a: Prosody I don't really I 'm not really sure what prosody means , in this context , so I just made up you know whether whether what they say is or h how they say it is is that . grad a: the Parse would be what verb they chose , and then maybe how they modified it , in the sense of whether they said " I need to get there quickly " or whatever . grad a: And  , in terms of World Knowledge , this would just basically be like opening and closing times of things , the time of day it is , and whatnot . grad d: What 's " tourbook " ? grad a: Tourbook ? That would be , I don't know , the " landmark - iness " of things , grad d:   So let me see if I can ask grad a: Well , this is not a working Bayes - net . No , I understand that , but but  So , what Let 's slide back up again and see start at the at the bottom and Oop - bo - doop - boop - boop . So , you could imagine w  , go ahead , you were about to go up there and point to something . grad a:  I I 'd No , I was gonna wait until professor b: Oh , OK . So , so if you if we made if we wanted to make it into a a real  Bayes - net , that is , you know , with fill you know , actually f  , fill it @ @ in , then  grad a: So we 'd have to get rid of this and connect these things directly to the Mode . And and  Bhaskara and I was talking about this a little earlier today is , if we just do this , we could wind up with a huge  , combinatoric input to the Mode thing . And  grad a: Well I oh yeah , I unders I understand that , I just  it 's hard for me to imagine how he could get around that . Let me just mention something that I don't want to pursue today which is there are technical ways of doing it ,  I I slipped a paper to Bhaskara and about Noisy - OR 's and Noisy - MAXes and there 're ways to  sort of back off on the purity of your Bayes - net - edness . If you co you could ima and I now I don't know that any of those actually apply in this case , but there is some technology you could try to apply . grad a: So it 's possible that we could do something like a summary node of some sort that OK . grad a: So in that case , the sum we 'd have we  , these wouldn't be the summary nodes . We 'd have the summary nodes like where the things were I guess maybe if thi if things were related to business or some other professor b: Yeah . professor b: So what I was gonna say is is maybe a good at this point is to try to informally  , not necessarily in th in this meeting , but to try to informally think about what the decision variables are . So , if you have some bottom line  decision about which mode , you know , what are the most relevant things . professor b: And the other trick , which is not a technical trick , it 's kind of a knowledge engineering trick , is to make the n each node sufficiently narrow that you don't get this combinatorics . So that if you decided that you could characterize the decision as a trade - off between three factors , whatever they may be , OK ? then you could say " Aha , let 's have these three factors " , OK ? and maybe a binary version f for each , or some relatively compact decision node just above the final one . professor b: And then the question would be if if those are the things that you care about ,  can you make a relatively compact way of getting from the various inputs to the things you care about . So that y so that , you know , you can sort of try to do a knowledge engineering thing grad a: OK . professor b: given that we 're not gonna screw with the technology and just always use  sort of orthodox Bayes - nets , then we have a knowledge engineering little problem of how do we do that .  and grad a: So what I kind of need to do is to take this one and the old one and merge them together ? professor b: " Eh - eh - eh .  , so  , Robert has thought about this problem f for a long time , cuz he 's had these examples kicking around , so he may have some good intuition about you know , what are the crucial things . professor b: and , I understand where this the  this is a way of playing with this abs Source - path - goal trajector exp   abstraction and and sort of sh displaying it in a particular way . professor b: I don't think our friends  on Wednesday are going to be able to Well , maybe they will . This is sort of th the second version and I I I look at this maybe just as a , you know , a a whatever , UML diagram or , you know , as just a  screen shot , not really as a Bayes - net as John Johno said . grad a: We could actually , y yeah draw it in a different way , in the sense that it would make it more abstract . But the  the the nice thing is that you know , it just is a is a visual aid for thinking about these things which has comple clearly have to be specified m more carefully professor b: Alright , well , le let me think about this some more , grad d: and  professor b: and  see if we can find a way to present this to this linguists group that that is helpful to them . grad d: ultimately we we may w w we regard this as sort of an exercise in in thinking about the problem and maybe a first version of  a module , if you wanna call it that , that you can ask , that you can give input and it it 'll  throw the dice for you ,  throw the die for you , because  I integrated this into the existing SmartKom system in in the same way as much the same way we can  sort of have this  this thing . So if this is what M - three - L  will look like and what it 'll give us ,  And a very simple thing . We have an action that he wants to go from somewhere , which is some type of object , to someplace . grad d: And this these  this changed now only  ,  It 's doing it twice now because it already did it once .  , we 'll add some action type , which in this case is " Approach " and could be , you know , more refined  in many ways . grad d: Or we can  have something where the  goal is a public place and it will give us then of course an action type of the type " Enter " . So this is just based on this one  , on this one feature , and that 's that 's about all you can do . And so in the f if this pla if the object type  here is is a m is a landmark , of course it 'll be  " Vista " . And  this is about as much as we can do if we don't w if we want to avoid   a huge combinatorial explosion where we specify " OK , if it 's this and this but that is not the case " , and so forth , it just gets really really messy . You 're you 're grad d:  ? professor b: It was much too quick for me . So , I I do understand that  you can take the M - three - L and add not and it w and you need to do this , for sure , we have to add , you know , not too much about  object types and stuff , and what I think you did is add some rules of the style that are already there that say " If it 's of type " Landmark " , then you take you 're gonna take a picture of it . Ev - every landmark you take a picture of , grad d: Every public place you enter , and statue you want to go as near as possible . grad d: W professor b: that 's a that 's another kind of baseline case , that 's another sort of thing " OK , here 's a another kind of minimal  way of tackling this " . Add extra properties , a deterministic rule for every property you have an action , " pppt ! " You do that .  , then the question would be  Now , if that 's all you 're doing , then you can get the types from the ontology , OK ? because that 's all you 're all you 're using is this type the types in the ontology and you 're done . grad d:  ? professor b: Right ? So we don't we don't use the discourse , we don't use the context , we don't do any of those things . professor b: Alright , but that 's but that 's OK , and  it it 's again a kind of one minimal extension of the existing things . And that 's something the  SmartKom people themselves would they 'd say " Sure , that 's no problem you know , no problem to add types to the ont " Right ? grad d: Yeah . And this is just in order to exemplify what what we can do very , very easily is ,  we have this this silly  interface and we have the rules that are as banal as of we just saw , and we have our content . grad d: Now , the content I whi which is sort of what what we see here , which is sort of the Vista , Schema , Source , Path , Goal , whatever . grad d: This will  be  a job to find ways of writing down Image schema , X - schema , constructions , in some some form , and have this be in a in a in the content , loosely called " Constructicon " . And  and here is exactly where what 's gonna be replaced with our Bayes - net , which is exactly getting the input feeding into here . This decides whether it 's an whether action the the Enter , the Vista , or the whatever professor b: " approach " , you called it , I think this time . This is  so what we 'd be generating would be a reference to a semantic  like parameters for the for the X - schema ? professor b: For for for Yes . So that that  i if you had the generalized " Go " X - schema and you wanted to specialize it to these three ones , then you would have to supply the parameters . professor b: And then  , although we haven't worried about this yet , you might wanna worry about something that would go to the GIS and use that to actually get you know , detailed route planning . professor b: But , presumably that that that functionality 's there when when we grad a: So the immediate problem is just deciding w which grad d: Aspects of the X - schema to add . professor b: Yeah , so the pro The immediate problem is is back t t to what you were what you are doing with the belief - net . professor b: You know ,  what are we going to use to make this decision grad a: Right and then , once we 've made the decision , how do we put that into the content ? professor b: Yeah . professor b: The harder problem is we decide what we want to use , how are we gonna get it ? And that the the that 's the hardest problem . So , the hardest problem is how are you going to get this information from some combination of the what the person says and the context and the ontology . The h So , I think that 's the hardest problem at the moment is is grad a: OK .  , and that 's so , getting back to here , we have a d a technical problem with the belief - nets that we we don't want all the com grad a: There 's just too many factors right now . professor b: So we wanna think about which ones we really care about and what they really most depend on , and can we c you know , clean this this up to the point where it grad a: So what we really wanna do i cuz this is really just the three layer net , we wanna b make it expand it out into more layers basically ? professor b: Right .  , it 's true that the way you have this , a lot of the times you have what you 're having is the values rather than the variable . So instead of in instead it should really be just be " intention " as a node instead of " intention business " or " intention tour " . professor b: OK ? So you Yeah , right , and then it would have values , " Tour " , " Business " , or  " Hurried " . professor b: But then but i it still some knowledge design to do , about i how do you wanna break this up , what really matters . grad a: I think what was going through my mind when I did it was someone could both have a business intention and a touring intention and the probabilities of both of them happening at the same time professor b: Well , you you could do that . And it 's perfectly OK to  insist that that , you know , th  , they add up to one , but that there 's  that that it doesn't have to be one zero zero . So you could have the conditional p So the each of these things is gonna be a a a probability . So whenever there 's a choice ,  so like landmark - ness and usefulness , grad a: Well , see I don't think those would be mutually professor b: OK grad a: it seems like something could both be professor b: Absolutely right . professor b: And so that you might want to then have those b Th - Then they may have to be separate . professor b: So that 's but again , this is this is the sort of knowledge design you have to go through . It 's you know , it 's great is is , you know , as one step toward  toward where we wanna go . grad d: Also it strikes me that we we m may want to approach the point where we can sort of try to find a  , a specification for some interface , here that  takes the normal M - three - L , looks at it . Then we discussed in our pre - edu EDU meeting  how to ask the ontology , what to ask the ontology  the fact that we can pretend we have one , make a dummy until we get the real one , and so  we we may wanna decide we can do this from here , but we also could do it  you know if we have a a a belief - net interface . But this information is just M - three - L , and then we want to look up some more stuff in the ontology and we want to look up some more stuff in the maybe we want to ask the real world , maybe you want to look something up in the GRS , but also we definitely want to look up in the dialogue history  some s some stuff . Based on we we have  I was just made some examples from the ontology and so we have for example some information there that the town hall is both a a a building and it has doors and stuff like this , but it is also an institution , so it has a mayor and so forth and so forth and we get relations out of it and once we have them , we can use that information to look in the dialogue history , " were any of these things that that are part of the town hall as an institution mentioned ? " , professor b:   grad d: " were any of these that make the town hall a building mentioned ? " , grad c: Right . So this may be a a sort of a process of two to three steps before we get our vector , that we feed into the belief - net , professor b: Yeah . grad d: and then professor b: There will be rules , but they aren't rules that come to final decisions , they 're rules that gather information for a decision process . So they 'll they presumably there 'll be a thread or process or something that " Agent " , yeah , " Agent " , whatever you wan wanna say , yeah , that  is rule - driven , and can can  can do things like that . And  there 's an issue about whether there will be that 'll be the same agent and the one that then goes off and  carries out the decision , so it probably will . My guess is it 'll be the same basic agent that  can go off and get information , run it through a a c this belief - net that turn a crank in the belief - net , that 'll come out with s  more another vector , OK , which can then be  applied at what we would call the simulation or action end . So on once you pull that out , it could be that that says " Ah ! Now that we know that we gonna go ask the ontology something else . " OK ? Now that we know that it 's a bus trip , OK ? we didn't We didn't need to know beforehand ,  how long the bus trip takes or whatever , but but now that we know that 's the way it 's coming out then we gotta go find out more . So this is actually , s if if we were to build something that is  , and , I had one more thing , the it needs to do Yeah . I think we I I can come up with a a code for a module that we call the " cognitive dispatcher " , which does nothing , professor b: OK . grad d: but it looks of complect object trees and decides how are there parts missing that need to be filled out , there 's this is maybe something that this module can do , something that this module can do and then collect  sub - objects and then recombine them and put them together . So maybe this is actually some some useful tool that we can use to rewrite it , and  get this part , professor b: Oh , OK . In particular see what we 'd like to do , and and this has been implicit in the discussion , is to do this in such a way that you get a lot of re - use . What you 're trying to get out of this deep co cognitive linguistics is the fact that w if you know about source source , paths and goals , and nnn all this sort of stuff , that a lot of this is the same , for different tasks . And that  there 's there 's some some important generalities that you 're getting , so that you don't take each and every one of these tasks and hafta re - do it . grad d: There 're no primitives upon which  professor b: u u What are the primitives , and how do you break this grad d: yeah . professor b: So I y I 'm just just there saying eee well you I know how to do any individual case , right ? but I don't yet see what 's the really interesting question is can you use  deep  cognitive linguistics to get powerful generalizations . professor b:  grad d: Maybe we sho should we a add then the " what 's this ? " domain ? N  , we have to " how do I get to X " . Then we also have the " what 's this ? " domain , where we get some slightly different professor b: Could . grad d:  Johno , actually , does not allow us to call them " intentions " anymore . professor b: Well , I I don't like the term either , so I have n i  i i y w i i It  grad d: But  , I 'm sure the " what 's this ? " questions also create some interesting X - schema aspects . I 'm not a I 'm not op particularly opposed to adding that or any other task , grad d: So . professor b: I 'm just saying that I 'm gonna hafta do some sort of first principles thinking about this . Well , no the Bayes the Bayes - nets The Bayes - nets will be dec specific for each decision . But what I 'd like to be able to do is to have the way that you extract properties , that will go into different Bayes - nets , be the  general . So that if you have sources , you have trajectors and stuff like that , and there 's a language for talking about trajectors , you shouldn't have to do that differently for   going to something , than for circling it , for  telling someone else how to go there , grad d: Getting out of professor b: whatever it is . So that that , the the decision processes are gonna be different What you 'd really like of course is the same thing you 'd always like which is that you have  a kind of intermediate representation which looks the same o over a bunch of inputs and a bunch of outputs . So all sorts of different tasks and all sorts of different ways of expressing them use a lot of the same mechanism for pulling out what are the fundamental things going on . And pushing it one step further , when you get to construction grammar and stuff , what you 'd like to be able to do is say you have this parser which is much fancier than the parser that comes with  SmartKom , i that that actually uses constructions and is able to tell from this construction that there 's  something about the intent you know , the actual what people wanna do or what they 're referring to and stuff , in independent of whether it about what is this or where is it or something , that you could tell from the construction , you could pull out deep semantic information which you 're gonna use in a general way . You might be able to to  say that this i this is the kind of construction in which the there 's Let 's say there 's a  cont there the the land the construction implies the there 's a con this thing is being viewed as a container . So just from this local construction you know that you 're gonna hafta treat it as a container you might as well go off and get that information . So if you say " how do I get into the castle " OK , then  Or , you know , " what is there in the castle " or so there 's all sorts of things you might ask that involve the castle as a container and you 'd like to have this orthogonal so that anytime the castle 's referred to as a container , you crank up the appropriate stuff . professor b: Alright , so that 's that 's the that 's the thesis level grad d:   professor b:  grad d: It 's unfortunate also that English has sort of got rid of most of its spatial adverbs because they 're really fancy then , in in for these kinds of analysis . professor b: Well , you have prepositional phrases that grad d: Yeah , but they 're they 're easier for parsers . grad d: Parsers can pick those up but but the with the spatial adverbs , they have a tough time . Oh yeah , b But an architecture like this would also enable us maybe to to throw this away and and replace it with something else , or whatever , so that we have so that this is sort of the representational formats we 're we 're we 're talking about that are independent of the problem , that generalize over those problems , and are oh , t of a higher quality than an any actual whatever  belief - net , or " X " that we may use for the decision making , ultimately . So , are we gonna be meeting here from now on ? I 'm I 'm happy to do that . We we had talked about it , cuz you have th th the display and everything , that seems fine . grad d: so far I think it was nice as a visual aid for some things and and professor b: Oh yeah . No I I think it 's worth it to ass to meet here to bring this , and assume that something may come up that we wanna look at . grad d: Yeah ? The   , she w she was definitely good in the sense that she she showed us some of the weaknesses professor b: Right . grad d: and  also the  the fact that she was a real subject you know , is is professor b: Right . Yeah , and and and yeah and and she took it seriously and stuff l No , it was great . grad d: So I think that   , w Looking just looking at this data , listening to it , what can we get out of it in terms of our problem , for example , is , you know , she actually m said you know , she never s just spoke about entering , she just wanted to get someplace , and she said for buying stuff . grad d: and in the other case , where she wanted to look at the stuff at the graffiti , also , of course , not in the sentence " How do you get there ? " was pretty standard . Nuh ? except that there was a nice anaphora , you know , for pointing at what she talked about before , and there she was talking about looking at pictures that are painted inside a wall on walls , so grad c: Right .  , because graffiti is usually found on the outside and not on the inside , grad c: Yeah