So , before we get started with the , technical part , I just want to review what I think is happening with the our data collection . Th - this this is s should be im it isn't There 's another thing going on of gathering data , and that 's pretty much independent of this . What we think is gonna happen is that , in parallel starting about now we 're gonna get Fey to , where you 're working with me and Robert , draft a note that we 're gonna send out to various CogSci c and other classes saying , " here 's an opportunity to be a subject . " And then there 'll be a certain number of  , hours during the week which she will be available and we 'll bring in people .  , roughly how many , Robert ? We d Do we know ? grad c: fifty was our sort of our first professor b: OK . So , we 're looking for a total of fifty people , not necessarily by any means all students but we 'll s we 'll start with with that . And , so , I guess there 's a plan to have a meeting Friday afternoon  , with  , Jane , and maybe Liz and whoever , on actually getting the script worked out . But what I 'd like to do , if it 's O K , is to s to , as I say , start the recruiting in parallel and possibly start running subjects next week . The week after that 's Spring Break , and maybe we 'll look for them some subjects next door grad c: Yeah . Also , F both Fey and I will , do something of which I may , eh kindly ask you to to do the same thing , which is we gonna check out our social infrastructures for possible subjects . So , if you happen to be sort of in a non - student social setting , and you know people who may be interested in being subjects We also considered using the Berkeley High School and their teachers , maybe , and get them interested in stuff . But I I will just make a first draft of the , note , the " write - up " note , send it to you and Fey and then professor b: And why don't you also copy Jane on it ? grad c: And , Are we Have we concurred that , these these forms are sufficient for us , and necessary ? professor b: th I think they 're necessary . professor b: and I think we 're just gonna use it as it is , and  grad c: N . There 's one tricky part about , they have the right  I The last paragraph " if you agree to participate you have the opportunity to have anything excised which you would prefer not to have included in the data set . " OK ? Now that , we had to be included for this other one which might have , meetings , you know , about something .  , so what I 'd like to do is also have our subjects sign a waiver saying " I don't want to see the final transcript " . professor b: And if they don't If they say " no , I 'm not willing to sign that " , then we 'll show them the final transcript . professor b: That ,  yeah , so we might actually ,  S i Jane may say that , " you know , you can't do this " , " on the same form , we need a separate form . I 'd I 'd I 'd like to , e e  , add an a little thi eh a thing for them to initial , saying " nah , do I don't want to see the final transcript . professor b: But other than that , that 's one 's been approved , this really is the same project , rec you know . grad c: And ,  and now it 's We have a complete English parser that does everything the German parser does . grad e: What did you end up having to do ?  , wha Was there anything interesting about it at all ? grad c: Well , if you , eh grad d: We 'll show you . professor b: Yeah , we can show us , grad e: or are we gonna see that ? professor b: right ? grad c: Well , w w We d The first we did is we we tried to to do change the the " laufen " into " run " , or " running " , or " runs " . grad c: So this sentence sentence is parses the p the same output , grad e:  . grad c: If if you 'd add add Today and Evening , it 'll add Time or not . grad e: And the t and the time , right ? grad c: So it i it does look at that . grad c: But all the rest is p simply frosting on the cake , and it 's optional for that parser . professor b: So , you can sho You you Are are you gonna show us the little templates ? grad c: And grad e: S grad c: Yeah . I I also have it running here , grad e: The former end g " Oh , I see . grad c: so if I do this now , you can see that it parsed the wonderful English sentence , " Which films are on the cinema today evening ? " But ,  . professor b: No i grad c: It could be " this evening , which which films are on the cinema " , or " running in the cinema , which "  , " today evening " ,  i " Is anything happening in the cinema this evening ? " grad e: OK . professor b: Well grad c: Ge - elaborate , or , more or less ,  professor b: Actually , it 's a little tricky , in that there 's some allowable German orders which aren't allowable English orders and so forth . So it it doe I it These u these optional elements , grad c: It is not professor b: it 's it 's actually a set , not a sequence ? grad c: Yeah . We were I was afraid that ,  professor b: Oh ! grad e: So it really is key word matching , basically . What 's an idiomatic of phrasing this ? Which films are showing ? grad d: Are pl playing at the cinema ? grad c: playing ? grad d: Yeah . grad e: Tonight ? grad d: I changed that file , actually , where it 's on my account . grad e: This this evening ? phd f: Actually , you would say , " which films are on tonight ? " grad d: You want to get it ? Or is di was it easy to get it ? grad c:  . grad c: Except that we d w we don't have this , time information here now , which is ,  Oh . And ,  you have already to make it a little bit more elaborate , right ? grad d: Yeah ,  I changed those sentences to make it , more , idiomatic . And , of course , you can have i many variations in those sentences , they will still parse fine . So , if you want to look at the templates , they 're conveniently located in a file , " template " . I had to change , @ @ " Spielfilm " to " film " , " Film " to " movie " , cinem " Kino " to " cinema " to " today " heu " heute " to " today " , grad e: Huh . grad c: evening " Abend " to " evening " professor b: Capitalized as well grad a:  . professor b: Y i grad d: One thing I was wondering , was , those functions there , are those things that modify the M - three - L basically ? grad c: Yep . grad c: And that 's that 's the next step , professor b: p grad c: but we 'll get to that in a second . professor b: So so , the point is , if it says " this " and " see " , it also will work in " see " and " this " ? grad e: S professor b: In the other order ? grad c: Yeah . professor b: with those two key words ? grad c: Should we try it ? professor b: " This is the one I want to see " or whatever . grad c: It 's correct , but I don't know where it gets it from . grad d:  it 's sort of grad c: And " see this " is exactly the same thing . grad d: One thing I was wondering was , those percentage signs , right ? So , why do we even have them ? grad c: Yep . grad d: Because if you didn't have them grad c: I 'll tell you why . grad c: And the value of the score is , v I assume , I guess , the more of these optional things that are actually in there , the higher the r score it is .  , let 's hope that the generation will not be more difficult , even though the generator is a little bit more complex .  but we 'll Mmm , that means we may need two hours and twenty minutes rather than an hour ten minutes , professor b: Alright . grad c: And the next thing I would like to be able to do , and it seems like this would not be too difficult either , is to say , " OK let 's now pretend we actually wanted to not only change the the mapping of of , words to the M - three - L but we also wanted to change add a new sentence type and and make up some some new M - three - L s " professor b: Yep . And , grad d: So , that 's grad c: that 's shouldn't be too tough . Yeah , so where are those those functions " Action " , " Goodbye " , and so on , right ? Are they actually , Are they going to be called ?  , are they present in the code for the parser ? grad c: Yeah . So what it probably does , is it takes the ,  Is this where it is ? This is already the XML stuff ? This is where it takes its own , syntax , and converts it somehow . Where is the  grad d: What are you looking for ? grad c: where it actually produces the the XML out of the , parsed stuff . You mean , where the where the act how the action " Goodbye " maps into something grad d: Yeah . So when it encounts encounters " Day " , it will , activate those h classes in the in the XML stuff But ,  I saw those actions  , the " Goodbye " stuff somewhere . So whatever n this does  this is , basically , looks l to me like a function call , right ? professor b:  ? Oh , yeah . grad c: And ,  So , whenever it it encounters " Goodbye " , which we can make it do in a second , here grad a: That function automatically generates an initialized XML structure ? grad c: I grad d: I think each of those functions act on the current XML structure , and change it in some way , for example , by adding a a l a field to it , or something . professor b: cause some of them there were other actions  , that that s seemed to step state variables somewhere , grad d: Right . professor b: and confirm that it 's grad c: W we  -  grad d: Oh , you mean that 's not going to actually modify the tree , professor b: I think that 's right . grad c: e mmm  , well i There is a feature called " Discourse - Status " , grad d: When there 's a feature . grad c: And so whenever I just say , " Write " , it will it will put this in here . professor b: Oh , so it always just Is it So it Well , go back , then , cuz it may be that all those th things , while they look like function calls , are just a way of adding exactly that to the XML . grad c: So , this professor b: e I 'm not sure e that grad c:  well , we we 'll see , when we say , let 's test something , " Goodbye " , causes it to c to create basically an " Action Goodbye - End - Action " . grad c: Now , if we know that " Write " produces a " Feature Discourse - Status Confirm Discourse - Status " . But there is some kind of function call , because how does it know to put Goodbye in Content , but , Confirm in Features ? grad c: Oh . It d it n That 's because grad d: So So , it 's not just that it 's adding that field . professor b: It 's it 's the It 's under what sub - type you 're doing it . grad c: Well , sometimes it m Sometimes , i grad d: Well , they 're defined somewhere , presumably . grad c: it grad a: Well , it just automatically initializes things that are common , right ? professor b:  grad a: So it 's just a shorthand . And there is , here , a reference So , this tells us that whatever is has the ID " zero " is referenced here by @ @ the restriction seed and this is exa " I want " What was the sentence ? professor b: " I want two seats here . Here , we change and so , we Here we add something to the Discourse - Status , that the user wants to change something that was sort of done before And ,  and that , whatever is being changed has something to do with the cinema . grad a: So then , whatever takes this M - three - L is what actually changes the state , not the Yeah , OK . I definitely think it 's It 's worth the exercise of trying to actually add something that isn't there . grad c:  ? professor b:  Disc grad c: Sort of get a complete understanding of the whole thing . Then the next thing we talked about is actually , figuring out how to add our own tags , and stuff like that . So , this is  , what I got today is the the new  M - three - L for  , the Maps , professor b: Yep . grad c: and with some examples So , this is the XML and this is sort of what it will look like later on , even though it you can't see it on on this resolution . And this is what it sort of is the the structure of Map requests ,  also not very interesting , and here is the more interesting stuff for us , is the routes , route elements , and , again , as we thought it 's really simple . We have @ @ simple " from objects " and " to objects " and so forth , points of interest along the way And , I asked them whether or not we could ,  First of all , I was little bit It seemed to me that this m way of doing it is sort of a stack a step backwards from the way we 've done it before . professor b: S grad c: So these are these are professor b: So these are these are your friends back at EML . There 's there 's not seven committees , or anything , right ? grad c: No . And every every element of that e r r f of that Every segment we call a " route element " . And so , from A to B we cut up in three different steps , and every step has a " from object " where you start , a " to object " where y where you sort of end , and some points of interest along the way . What w I was sort of missing here , and  , maybe it was just me being too stupid , is , I didn't sort of get the the notion of the global goal of the whole route . And I suggested that they should n be k  , kind enough to do s two things for us , is one , Also allocating , some tags for our Action Schema Enter - Vista - Approach , and And also , since you had suggested that that , we figure out if we ever , for a demo reason , wanted to shortcut directly to the g GIS and the Planner , of how we can do it . Now , what 's the state of the art of getting to entrances , what 's the syntax for that , how get getting to vista points and calculating those on the spot . Wherever you 'll find a route planner it n does nothing but get to the closest point where the street network is at minimal distance to the geometric center . Let ,  I want a a Again , outside of m almost managerial point ,  You 're in the midst of this , so you know better . But it seems to me it 's probably a good idea to li  minimize the number of  , change requests we make of them . Does this does this make sense to you guys ? It  you 're you 're doing the the interaction but it seemed to me that what we ought to do is come up with a  , something where you ,  And I I don't know who 's mok working most closely on it .  , take what they have , send it to everybody saying " this is what they have , this is what we think we should add " , OK ? and then have a d a an iteration within our group saying "  , well " OK ? And get our best idea of what we should add . Is i or , I don't know does this make sense to you ? Or grad c: Yeah . Especially if we want Sort of , what I my feeling was eh we we sort of reserved something that has a r eh an OK label . grad c: I w No matter how we want to call it , this is sort of our playground . grad c: And if we get something in there that is a structure elaborate and and and and and complex enough to to to maybe enable a whole simulation , one of these days , that would be u the the perfect goal . What are the thl class of things we think we might try to do in a year or two ? How how would we try to characterize those and what do we want to request now that 's leave enough space to do all that stuff ? grad c:   professor b: And so that sounds like a great thing to do as the priority item  , as soon as we can do it . professor b: So y so you guys will send to the rest of us  a version of  , this , and the  , description grad a: With sugge yeah , suggested improvements and professor b: Well b Yeah . So , the the  Not everyone  , reads German , so if you 'd  grad a: Mmm . Then , with some sug s suggestions about where where do we go from here ? grad a: OK . But for the moment just  , t for this class of of things , we might want to try to encompass . This is this is everything that that , you know ,  we might want to do in the next couple years . So I just this XML stuff here just has to do with Source - Path - Goal type stuff , in terms of traveling through Heidelberg . grad a: So , but this O Is the domain greater than that ? professor b: No . It 's beyond Source - Path - Goal , but I think we don't need to get beyond it @ @ tourists in Heidelberg . professor b: It seems to me we can get all the complexity we want in actions and in language without going outside of tourists in Heidelberg . OK ? But you know , i depending on what people are interested in , one could have , tours , one could have  , explanations of why something is is , you know , why why was this done , or  , no there 's no end to the complexity you can build into the  , what a tourist in Heidelberg might ask . professor b: So , at least unless somebody else wants t to suggest otherwise I think the general domain we don't have t to  , broaden . And if there 's something somebody comes up with that can't be done that way , then , sure . W we 'll we 'll look at that , but  I 'd be s I I 'd be surprised at if there 's any important issue that that And ,   if if you want to  , push us into reference problems , that would be great . professor b: and you know , what what are these things referring to ? Not only anaphora , but , more generally the ,  this whole issue of , referring expressions , and , what is it that they 're actually dealing with in the world ? grad c:   professor b: And , again , this is li in the databa this is also pretty well formed because there is an ontology , and the database , and stuff . professor b: There aren't really deep mysteries about  , what w what things the system knows about . professor b: And a l and a lot and and anaphora , and pronouns , grad c: Nuh . grad c: Now , we hav the the whole Unfortunately , the whole database is , in German . We have just commissioned someone to translate some bits of it , IE the e the shortest k the the more general descriptions of all the objects and , persons and events . But did y I  I think there will be great because the reference problem really is not trivial , even if you have such a g well - defined world . grad a: Could you give me an example of a reference problem ? so so l I can make it more concrete ? grad c: Well How do I get to the Powder - Tower ? We sort of t think that our bit in this problem is interesting , but , just to get from Powder - Tower to an object I ID in a database is also not really trivial . phd f: Or or if you take something even more scary , " how do I get to the third building after the Tower ? the Ple - Powder - Tower ? " grad a: Mmm . Or , you know , the church across from City Hall , or grad a: Or the re the restaurant where they wear lederhosen ? grad c: Or the phd f: Right . grad c: O or or tower , or this tower , or that building , or phd f: Right . Trying to professor b: Or you can say " how " you know , " how do I get back ? " grad a: Yeah , yeah . And , again , it 's just a question of which of these things , people want to dive into . What , I think I 'm gonna try to do , and I guess , pwww ! let 's say that by the end of spring break , I 'll try to come up with some general story about , construction grammar , and what constructions we 'd use and how all this might fit together . grad c: u u u u That 's what strikes me , that we sort of the de g  , small Something , maybe we should address one of these days , is to That most of the work people actually always do is look at some statements , and and analyze those . grad c: But the whole i is it is it really relevant that we are dealing mostly with , sort of , questions ? professor b: Oh , yeah ? grad c: you know professor b: Well ,  yeah , I d grad c: And this is It seems to me that we should maybe at least spend a session or or brainstorm a little bit about whether that l this is special case in that sense . You know Did we ever find m metaphorical use in in questions in in that sense , really ? professor b: Yeah . grad c: " Who got kicked out of France ? " phd f: Muh professor b: Yeah , or , you know . " Wh - why is he why is he pushing for promotion ? " grad c: Nuh . professor b: er , just pick pick any of them and just do the eh grad c:   professor b: So I don't I don't think , it 's at all difficult  , to convert them to question forms that really exist and people say all the time ,  And sort of we don't know how to handle them , too . Right ?  , it 's I d It We don't know how to handle the declarative forms , @ @ really , and , then , the interrogative forms , ah - oh . grad d: Ooo ! professor b: Nancy , it looked like you were s grad e: Oh . it 's just that that the goals are g very different to cases So we had this problem last year when we first thought about this domain , actually , was that most of the things we talked about are our story understanding . grad e: we 're gonna have a short discourse and the person talking is trying to , I don't know , give you a statement and tell you something . And here , it 's th grad c: Help you create a mental model , blah - blah - blah . grad e: And then here , y you are j  , the person is getting information and they or may not be following some larger plan , you know , that we have to recognize or , you know , infer . And th th the their discourse patterns probably don't follo follow quite as many logical connec professor b: Right . No , I think that 's one of things that 's interesting , is is in this sort of over - arching story we we worked it out for th as you say , this the storytelling scenario . Cuz for a while we were thinking , " well , how can we change the , data to sort of illicit tha illicit , actions that are more like what we are used to ? " But obviously we would rather , you know , try to figure out what 's what 's , you know professor b: Well , I don't know .  , maybe maybe that 's what we 'll do is is s u e We can do anything we want with it . professor b: OK , and the one for next  , summer is just half done and then the other half is this , " generation thing " which we think isn't much different . professor b: So once that 's done , then all the rest of it is , sort of , you know , what we want to do for the research . And we can w we can do all sorts of things that don't fit into their framework at all . professor b: If we can use all the , execution engines , then we can , you know , really try things that would be too too much pain to do ourselves . So , if we want to turn it into u understan standing stories about Heidelberg , we can do that .  , that would just be a t a  grad c: Or , as a matter of fact , we need and if we if we ' r eh take a ten year perspective , we need to do that , because w e w a Assuming we have this , we we ta in that case we actually do have these wonderful stories , and historical anecdotes , professor b: Yeah . So , th the database is huge , and if we want to answer a question on that , we actually have to go one step before that , and understand that . grad c: And so , this has been a a a Deep Map research issue that was is is part of the unresolved , and to - do 's , and something for the future , is how can we sort of run our our text , our content , through a machine that will enable us , later , to retrieve or answer e questions more sensibly ? phd f: Mwa   S Who 's going ? phd f: So ,  So , I was just going to ask , so , what is the the basic thing that that you are , obligated to do , by the summer before w  y c we can move professor b: Ah ! OK . So , what happened is , there 's this , eh ,  Robert was describing the There 's two packages there 's a , quote parser , there 's a particular piece of this big system , which , in German , takes these t sentence templates and produces XML structures . professor b: The other thing is , at the other end , roughly at the same level , there 's something that takes , X M L structures , produces an output XML structure which is instructions for the generator . professor b: OK ? And then there 's a language generator , and then after that a s a synthesizer that goes from an XML structure to , language generation , to actual specifications for a synthesizer . Eh , but again , there 's one module in which there 's one piece that we have to convert to English . And that But as I say , this is all along was viewed as a kind of a m a minor thing , necessary , but but not phd f: Right . professor b: And much more interesting is the fact that , as part of doing this , we we are , you know , inheriting this system that does all sort of these other things . professor b: Not precisely what we want , and that 's that 's wh where it it gets difficult . So , e enough of that , but I , mmm , the e sort of , Johno and I will take up that responsibility , and , get a first draft of that . grad c: y you guys sort of started fighting , on the Bayes - net " Noisy - OR " front ? grad d:  . Yeah , I thought I should , talk a little bit about that , because that might be a good , sort of architecture to have , in general for , problems with , you know , multiple inputs to a node . And what 's the other one ? so that just we know what the d agenda is ? grad c: the Wu paper , I think maybe professor b: Oh , yeah .  , so I I 've been in contact with Wu , so , probably let 's put that off till I I till I understand better , what he 's doing . It 's just a little embarrassing cause all this was in his thesis and I was on his thesis committee , and , so , I r really knew this at one time . professor b: But , I I It 's not only  Is Part of what I haven't figured out yet is is how all this goes together . professor b: squealing sound ? grad d: Or shall I just use this ? professor b: It 's probably just as easy . grad d: Hey ! grad a: I was envying you and your pocket cause I don't have one . grad e: It was a quick one , huh ? professor b: That 's why they invented " pocket T 's " . grad d: So ,  Recall that , we want to have this kind of structure in our Bayes - nets . Namely , that ,  You have these nodes that have several bands , right ? So Does  , they sort of the typical example is that , these are all a bunch of cues for something , and this is a certain effect that we 'd like to conclude . So ,  Like , let 's just look at the case when , this is actually the the final action , right ? So this is like , you know , touch , grad c: Y grad d: or grad c: E - EVA grad d: Sorry .  grad c:  grad d: Yeah , E - EVA , right ? grad c: Yeah . grad d: Enter , V View , Approach , right ? phd f: W what was this ? It i i i ehhh , i ehhh . professor b: Wri - write it out for for grad d: So , this is Yeah . So , we 'd like to take all these various cues , right ? phd f: Like the army . grad d: So this one might be , say ,  grad e: New terminology ? phd f: Yeah . grad c:  ? grad d: Well , let me pick a random one grad e: I haven't heard that before . grad d: and say ,  I don't know , it could be , like This isn't the way it really is , but let me say that , suppose someone mentioned , admission fees Ah , it takes too long . If the thing is a landmark , you know ,  then there 's another thing that says if  if it 's closed or not , at the moment . Right ? And the , problem that we were having was that , you know , given N - nodes , there 's " two to the N " Given N - nodes , and furthermore , the fact that there 's three things here , we need to specify " three times " , " two to the N " probabilities . For example , they could be " time of day " , in which case we could , say , you know , " Morning , afternoon , evening , night " . And , that 's a lot of probabilities to put here , which is kind of a pain .  Where should I put this ? So , the idea is that , Let 's call these , C - one , C - two , C - three , and C - four , and E , for Cause and Effect , I guess . Well , actually , the idea , first of all , is that each of these things has a quote - unquote distinguished state , which means that this is the state in which we don't really know anything about it . So right ? So , for example , if we don't really know if the thing is a landmark or not , Or , i if that just doesn't seem relevant , then that would be th sort of the Disting - the Distinguish state . It 's a really , you know , if there is something for the person talking about the admission fee , you know , if they didn't talk about it , that would be the Distinguish state . grad c: S so , this is a fanciful way of saying " default " ? grad d: So Yeah , yeah . grad d: So , the idea is that , you have these intermediate nodes , right ? E - one , E - two , E - three and E - four ? professor b: So , this is the Heckerman paper you 're working with ? Good . So The idea is that , each of these EI is represents what this would be if all the other ones were in the distinguish state . Right ? So , for example , suppose that the person  , suppose the thing that they talked about is a landmark . Then , this would be W The this would just represent the probability distribution of this , assuming that this cue is turned on and the other ones just didn't apply ? So , you know , if it is a landmark , and no none of the other things really ap applicable , then this would represent the probability distribution . So maybe in this case Maybe we just t k Maybe we decide that , if the thing 's a landmark and we don't know anything else , then we 're gonna conclude that ,  They want to view it with probability , you know , point four . They want to enter it with probability ,  with probability point five and they want to approach it probability point one , say Right ? So we come up with these l little tables for each of those OK . And the final thing is that ,  this is a deterministic function of these , so we don't need to specify any probabilities . We just have to , say what function this is , right ? So we can let this be ,  G of E - one comma E - two . The important point is that  There is a a a general kind of idea of shortcutting the full CPT . And So I gave eh Bhaskara a copy of this , eh sort of " ninety - two " paper . professor b: So , I u w  , yo  you Have you read it yet ? grad d: you can Yeah , you should take a look at it , I guess . professor b: And  One of the questions just come at Bhaskara is , " How much of this does JavaBayes support ? " grad d: Yeah , it 's a good question . grad d: And ,  In a sense it sup we can make it supported by , manually , entering , you know , probabilities that are one and zeros , right ? professor b: Right . So the little handout that The little thing that I sent I sent a message saying , here is a way to take One thing you could do , which is kind of s in a way , stupid , is take this deterministic function , and use it to build the CPT . So , if Ba - JavaBayes won't do it for you , grad c: Mmm .  and , what I sent out about a week ago , was an idea of how to do that , for , evidence combination . So one of one function that you could use as your " G function " is an e e Evidence - Combining . So you just take the  , if each of th if each of the ones has its own little table like that , then you could take the , strength of each of those , times its little table , and you 'd add up the total evidence for " V " , " E " , and " A " . professor b: I i i No , no But I 'm saying is There There is a w  , if y if if you decide what 's what is appropriate , is probablistic evidence combination , you can write a function that does it . It 's a pui it 's actually one of the examples he 's got in there . But , anyway , s skipping skipping the question of exactly which functions now is it clear that you might like to be able to shortcut the whole conditional probability table . grad c: in some it seems very plausible in some sense , where we will be likely to not be observe some of the stuff . That 's one of the problems , is , W Is is , Where would th Where would it all come from ? grad c: Yeah . grad d: What ? grad c: I if it 's a a a discar Discourse Initial Phrase , we will have nothing in the discourse history . A are you saying that we 'll not be able to observe certain nodes ? That 's fine . The f the the the Bayes - nets in general are quite good at saying , " if you have no current information about this variable just take the prior for that . So , if you don't have any information about the discourse , you just use your priors of of whatever eh the discourse  , eh , basically whatever w it 's Probabilistically , whatever it would be . But the other problem is , how do you fill in all these numbers ? And I think that 's the one he was getting at . So , specifically in this case you have to f have this many numbers , grad e: Yeah . grad d: whereas in this case you just have to have three for this , three for this , three for this . grad d: So you have to have just three N ? So , this is much smaller than that . professor b:  grad e: So , you don't need da data enough to cover  , nearly as much stuff . grad a: So , really , i What a A Noisy - OR seems to kind of " neural - net - acize " these Bayes - nets ? professor b: Eh well to some No , no . So , " Noisy - OR " is a funny way of referring to this , because the Noisy - OR is only one instance . So Eh  grad a: Well , my point was more that we just eh With the neural net , right , eh , things come in , you have a function that combines them and professor b: Yeah , it it Tha - that 's true . It is a is also more neural - net - like , although  , it isn't necessarily sum  , s you know , sum of weights or anything like that . professor b:  i You could have , like the Noisy - OR function , really is one that 's essentially says , take the max . So And , I thi I think that 's the standard way people get around the  There are a couple other ones . But ,  The I think we definitely I think it 's a great idea tha to to pursue that . It  you you can always  see easily that that I 'm not grasping everything correctly , but what seemed attractive to me in im  in the last discussion we had , was that we find out a means of of getting these point four , point five , point one , of C - four , not because , you know , A is a Landmark or not , but we we we label this whatever object type , and if it 's a garden , it 's point three , point four , point two . If it 's , a town hall , it 's point two , point three , point five . And we don't want to write this down necessarily every time for something but ,  let 's see . grad d: It 'll be students Where else would it be stored ? That 's the question . So , i is Well , let me say something , guys , cuz there 's not There 's a pretty point about this we might as well get in right now . professor b: So that  , if you know about it let 's say , a particular town hall that , it 's one that is a monument , then , that would be stored there . If you don't , you look up the hierarchy , Eh so , you you you may or So , then you 'd have this little vector of , you know , Approach Mode or EVA Mode . professor b: or , link to or but but in any case i View it logically as being in the ontology . professor b: And , if yo As I say , if you know about a specific object , you put it there . So , when we get to Wu , The - e We 'll see w what he says about that . professor b: And , then if you If it isn't there , it 's higher , and if you don't know anything except that it 's a b it 's it 's a building , then up at the highest thing , you have the pr what amounts to a prior . If you don't know anything else about a building , you just take whatever your crude approximation is up at that level , grad d: Right . professor b: So , that 's a very pretty relationship between these local vectors and the ontology . And it seems to me the obvious thing to do , unless we find a reason to do something different . professor b: Does this make sense to you ? grad d: So professor b: Bhask - ? grad d: Yeah . So , we are but we we 're not doing the ontology , so we have to get to whoever is doing the u ultimately , professor b: Indeed . So , that 's another thing we 're gonna need to do , is is , to , either grad d: we have to get them to professor b: We 're gonna need some way to either get a p tag in the ontology , or add fields , or some way to associate Or , w It may be that all we can do is , some of our own hash tables that it Th - the th you know , there 's always a way to do that . It 's a just a question of grad a: Yeah , hash on object name to , you know , the probabilities or whatever . And , so , i  grad c: But it 's ,  Well , it strikes me as a What For If we get the mechanism , that will be sort of the wonderful part . And then , how to make it work is is the second part , in the sense that  , m the guy who was doing the ontology eh , eh , s ap apologized that i it will take him another through two to three days because they 're having really trouble getting the upper level straight , and right now . The reason is , given the craw bet  , the the the projects that all carry their own taxonomy and , on all history , they 're really trying to build one top level ontology ft that covers all the EML projects , and that 's , sort of a tough cookie , a little bit tougher than they figured . But , nevertheless , it 's going to be there by n by , next Monday and I will show you what 's what some examples from that for towers , and stuff . And , what I don't think is ever going to be in the ontology , is sort of , you know , the likelihood of , eh , people entering r town halls , and looking at town halls , and approaching town halls , especially since we are b dealing with a case - based , not an instance - based ontology . So , there will be nothing on on that town hall , or on the Berkeley town hall , or on the Heidelberg town hall , it 'll just be information on town halls . professor b: Well , they they they How ar What are they gonna do with instances ? grad c: But what professor b: you y grad c: Well , that 's Hhh .  , th the first , they had to make a design question , " do we take ontologies that have instances ? or just one that does not , that just has the types ? " professor b: OK . grad c: And , so , since the d decision was on types , on a d simply type - based , we now have to hook it up to instances .  this is professor b: But what i What is SmartKom gonna do about that ? grad c: one professor b: Cuz , they have instances all the time . grad c: Yeah , but the ontology is really not a SmartKom thing , in in and of itself . professor b: I understand , but is anybody doing anything about it ? grad c:  professor b: OK . grad c: No , but th the r eh I th I still think that there is enough information in there . So , in a sense , " I " used as Institutions for some s in some sense or the other . When we see what people have done , it may turn out that the easiest thing to do is to build a a separate thing that that just pools i i Like , i i it it may be , that , the the instance w That we have to build our own instance , things , that , with their types , grad d: Yeah , it 's Right , we can just assume professor b: and then it goes off to the ontology once you have its type . So we build a little data structure And so what we would do in that case , is , in our instance gadget have our E V And if we d there isn't one we 'd get the type and then have the E V As for the type . professor b: So , we 'd have our own little things so that whenever we needed one , we 'd just use the ontology to get the type , grad d:   professor b: and then would hash or whatever we do to say , " ah ! grad d:   professor b: If it 's that type of thing , and we want its EVA vector , pppt - pppt ! it 's that . And then But , the combination functions , and whether we can put those in Java Bayes , and all that sort of stuff , is ,  is the bigger deal . grad a: We could just steal the classes in JavaBayes and then interface to them with our own code . grad d:  professor b: Well , I me ye eh , yeah , the grad d: That requires understanding the classes in JavaBayes , I guess .  , you 've been around enough to  Just ? grad a: Well , it depends on professor b: there 's this huge package which which may or may not be consistent and you know . It 's b It It 's an inter sort of a kind of a it The thing is , it 's kind of an interpreter and i i it expects its data structures to be in a given form , and if you say , " hey , we 're gonna make a different kind of data structure to stick in there " grad a: Well , no , but that just means there 's a protocol , right ? That you could professor b: It may or may not . That 's the question is " to what extent does it allow us to put in these G functions ? " And I don't know . grad a: Well , no , but  What I  the So you could have four different Bayes - nets that you 're running , and then run your own write your own function that would take the output of those four , and make your own " G function " , is what I was saying . professor b: Yeah , that 's fine if it 's if it comes only at the end . But suppose you want it embedded ? grad a: Well , then you 'd have to break all of your Bayes - nets into smaller Bayes - nets , with all the professor b: Oh , that Yeah , that 's a truly horrible way to do d it . But , at that point you may say , " hey , Java Bayes isn't the only package in town . Let 's see if there 's another package that 's , eh , more civilized about this . " grad d:  professor b: Now , Srini is worth talking to on this , grad d: Mmm . professor b: cuz he said that he actually did hack some combining functions into But he doesn't remember at least when I talked to him , he didn't remember whether it was an e an easy thing , a natural thing , or whether he had to do some violence to it to make it work . I don't see why the , combining f functions have to be directly hacked into  , they 're used to create tables so we can just make our own little functions that create tables in XML . professor b: Well , I say that 's one way to do it , is is to just convert it int into a into a C P T that you zip It 's blown up , and is a it 's ,  it 's huge , but it doesn't require any data fitting or complication . I don't think  , the fact that it blown u blows up is a huge issue in the sense that  , OK . So say it blows up , right ? So there 's , like , the you know , ten , f ten , fifteen , things . It said , " Here 's the way you 'd take the logical f G function and turn it into a CPT . So , I will , e e before next week , @ @ p push push some more on on this stuff that Dekai Wu did , and try to understand it .  , you 'll make a couple of more copies of the Heckerman paper to give to people ? grad d: p Sure . And I I 'll I 'll think s through this , eh getting EVA vectors dynamically out of ontologies one more time because I s I I I 'm not quite sure whether we all think of the same thing or not , here . professor b: Alright , great ! And , Robert , thank you for coming in under He he 's been sick , Robert . grad a: I was thinking maybe we should just cough into the microphone and see if they can't th see if they can handle it