grad a: Alright , so I 'm - I should read all of these numbers ? professor b: OK . professor b: OK , so  i  I don't know whether Ami 's coming or not  but I think we oughta just get started . grad e: Nancy is  currently in Berkeley but not here ? grad c: Nancy 's still stick ? professor b: Don't know . Anyway , so my idea f for today and we can  decide that that isn't the right thing to do was to at spend at least part of the time trying to eh build the influence links , you know which sets of things are  relevant to which decisions and actually I had  specific s suggestion to start first with the path ones . The database ones being in some sense less interesting to us although probably have to be done and so to do that so there 's and the idea was we were gonna do two things grad c: Is your mike on ? professor b: Ah . We were gonna do two things one of which is just lay out the influence structure of what we think influences what grad d: That 's funny . professor b: and then as a  separate but related task  particularly Bhaskara and I were going to try to decide what kinds of belief nodes are needed in order to  do what we what we need to do . Once so but du we should sort of have all of the  basic design of what influences what done before we decide exactly how to compute it . So I didn't did you get a chance to look at all yet ? grad d: Yeah , I looked at some of that stuff . OK so let 's start with the  belief - nets , the general influence stuff and then we 'll then we 'll also at some point break and talk about the techy stuff . grad e: Well I think one could go there 's I think we can di discuss everything . First of all this I added , I knew from sort of basically this has to be there right ?  professor b: Oh are you gonna go there or not ? Yeah , so one i grad e: Given given   not transverse the castle , the decision is does the person want to go there or is it just professor b: Right , true . And I 'm sure we 'll find more as we go that grad e: And  ? So Go - there in the first place or not is definitely  one of the basic ones .  Is this basically true or false or maybe we 'll get professor b: Well grad d: Which one ? grad e: what ? grad a: " Go there " . professor b: so there is this question about grad e: Here we we actually get just probabilities , professor b: Yeah . professor b: the the reason it might not be true or false is that we did have this idea of when so it 's , you know  current @ @ and so forth and so on or not at all , grad e:   professor b: right ? And so that a decision would be do we want that so you could two different things you could do , you could have all those values for Go - there or you could have Go - there be binary and given that you 're going there when . grad e:  ? grad a:  it seems that you could   it seems that those things would be logically independent like you would wanna have them separate or binary , Go - there and then the the possibilities of how to go there because professor b: OK , that 's let 's start that way . grad a: because , you know it might be easy to figure out that this person is going to need more film eventually from their utterance but it 's much more complex to query when would be the most appropriate time . And so I 've tried to come up with some initial things one could observe so who is the user ? Everything that has user comes from the user model everything that has situation comes from the situation model - A . But when it comes to sort of writing down when you when you do these things is it here ? You sort of have to a write the values this can take . grad e: And here I was really  in some s sometimes I was really sort of standing in front of a wall feeling very stupid because  this case it 's pretty simple , but as we will see the other ones  for example if it 's a running budget so what are the discrete values of a running budget ? So maybe my understanding there is too impoverished . professor b: No  grad e: How can I write here that this is something , a number that cr keeps on changing ? But OK . professor b: You 've s have you seen this before at all Keith , these belief - net things ? grad a: no , but I think I 'm following it . grad e: So here is the the we had that the user 's budget may influence the outcome of decisions . grad d: Is this like a number that represents how much money they have left to spend ? OK , h well  how is it different from user finance ? grad e:  the finance is sort of here thought of as as the financial policy a person carries out in his life , he is he cheap , average , or spendy ? grad d: Alright . grad e: And  I didn't come  maybe a user I don't know , I didn't want to write greediness , but grad a: Yeah . So Keith w what 's behind this is actually a program that will once you fill all this in actually s solve your belief - nets for you and stuff . professor b: So this is not just a display , this is actually a GUI to a simulator that will if we tell it all the right things we 'll wind up with a functioning belief - net at the other end . grad e: OK , so here was OK , I can think of  people being cheap , average , or spendy or we can even have a a finer scale moderately cheap , professor b: Doesn't matter . grad d: Well ,  you 've written in you 've written in what  seems to be required like what else is is do you want ? grad e: If that 's permissible then I 'm happy . So here 's here 's what 's permissible is that you can arrange so that the  the value of that is gonna have to be updated and n it 's not a belief update , right ? It 's you took some actions , you spent money and stuff , so the update of that is gonna have to be essentially external to the belief - net . And if it does influence anything then you 're gonna need something that converts from the the number here to something that 's relevant to the decision there . So it could be ra they create different ranges that are relevant for different decisions or whatever but for the moment this is just a node that is conditioned externally and might influence various things . grad e: OK , and so this , oh that grad d: The other thing is that  every time that 's updated beliefs will have to be propagated but then the question is do you do we wanna propagate beliefs every single time it 's updated or only when we need to ? professor b: Yeah , that 's a good question . grad d:  Well , in Srini 's thing there was this thing there was this  option like proper inferences which suggests that  doesn't happen , automatically . Yeah someone has to track that down , but I but  And and and I think actually  grad e: I just accidentally Oops . professor b: one of the we w items for the  user home base  should be  essentially non - local . I they 're only there for the day and they don't have a place that they 're staying . grad d: Well grad e: Oh just  accidentally erased this , I I just had values here such as   is he s we had in our list we had " Is he staying in our hotel ? " , " Is he staying with friends ? " , and so forth professor b: Yeah . So my suggestion is we just pick  grad e: Something down here ? professor b: one , you know one  particular one of the  well let 's do the first first one let 's do the one that we sort of already think we did so w that was the of the endpoint ? grad e:   grad d: Is  grad e: Ah , grad d: So it 's true or false ? professor b: No , that 's that 's a grad e: OK . grad c: What 's the difference between mode and endpoint ? grad d: I thought mode , yeah . professor b: although that grad e:  mode was  professor b: Well , that 's grad d: Mode of transportation ? grad e: Yeah . professor b: No , he has he hasn't filled them in yet , is what 's true . grad e: climb , emerge professor b: No no no , these are ju that 's just a point , grad c:  grad d: Well some of those are subsumed by approach . professor b: this is ju grad c: Would it be an endpoint if you were crossing over it ? grad a: The Charles Bridge , you know . You know , you y you go first go the town square grad c: Well I eh grad a: No , if you go to re you know if you go to Prague or whatever one of your your key points that you have to do is cross the Charles Bridge and doesn't really matter which way you cross which where you end up at the end but the part the good part is walking over it , so . Anyway so let 's just leave it three with three for now grad e:  -  , mmm . professor b: and let 's see if we can get it linked up just to get ourselves started . grad e: OK , we professor b: You 'll see it you 'll see something comes up immediately , that the reason I wanna do this . grad e: w well the  user was  definitely more likely to enter if he 's a local professor b: Right . grad e: more likely to view if he 's a tourist  and then of course we had the fact that given the fact that he 's thrifty and there will be admission then we get all these cross  professor b: We did , but the three things w that that it contributed to this in fact , the other two aren't up there . so one was the ontology grad e: We 'll d what type of building is it ? professor b: Yeah . professor b: OK , so this is w Right , so what w I what we seem to need here , this is why it starts getting into the technical stuff grad a:  -  professor b: the way we had been designing this , there were three intermediate nodes  which were the endpoint decision as seen from the  user model as seen from the ontology and as seen from the discourse . So each of those the way we had it designed , now we can change the design , but the design we had was there was a decision with the same three outcomes  based on the th those three separate considerations grad a:  -  professor b: so if we wanted to do that would have to put in  three intermediate nodes grad e:  we can load it up it you know very simple . grad a: So professor b: and then what you and I have to talk about is , OK if we 're doing that and they get combined somehow  how do they get combined ? But the they 're they 're undoubtedly gonna be more things to worry about . grad e: So that 's w w in our  in in Johno 's sort of pictogram everything that could contribute to whether a person wants to enter , view , or approach something . professor b: Oh , it was called mode , so this this is m mode here means the same as endpoint . professor b: OK , why don't we ch can we change that ? grad e: We can just rename that , yeah . You know , but that was actually , yeah unfortunately that was a  kind of an intermediate versio that 's I don't think what we would currently do . grad a: Can I ask about " slurred " and " angry " as inputs to this ? professor b: That 's a grad a: What why ? grad d: Like they 're either true or false grad e: The prosody ? grad a: OK . grad c: If the if the person talking is angry or slurs their speech they might be tired or , you know grad a:   grad d: Therefore grad c: And , you know , possibly  grad a: Less likely to enter . So so my advice to do is is get this down to what we think is actually likely to to be a a strong influence . professor b: So let 's think about this this question of how do we wanna handle so there 're two separate things . One is how do we want to handle the notion of the ontology now what we talked about , and this is another technical thing Bhaskara , is  can we arrange so that I think we can so that the belief - net itself has properties and the properties are filled in  from on ontology items . So the let 's take the case of the  this endpoint thing , the notion was that if you had a few key properties like is this a tourist site , you know some kind of landmark is it a place of business  is it something you physically could enter grad a:   So that there 'd be certain properties that would fit into the decision node and then again as part of the ou outer controlling conditioning of this thing those would be set , so that some somehow someone would find this word , look it up in the ontology , pull out these properties , put it into the belief - net , and then the decision would flow . professor b: Now grad e: Seems to me that we 've sort of e em embedded a lot , em embedded a lot of these  things we had in there previously in in in some of the other final decisions done here , for example if we would know that this thing is exhibiting something  professor b: Right . grad e: if it is exhibiting pictures or sculptures and stuff like this , then it 's more likely to be entered . professor b: I  that 's I think that 's completely right and  I think that 's good , right ? So what what that says is that we might be able to  take and in particular so so the ones we talked about were  exhibiting and selling grad e: Accessibility . professor b: no , accessibility meant grad e: If it 's closed one probably won't enter . Or if it 's not accessible to a tourist ever the likelihood of that person actually wanting to enter it , professor b: OK . grad e: Yeah , all all of these if it 's fixing things selling things , or servicing things professor b: Right . is that you want an intermediate structure which i  is essentially the or of  for this purpose of of  selling , f fixing , or servicing . So that it  that is , for certain purposes , it becomes important but for this kind of purpose  one of these places is quite like the other . Does that seem right ? So we di grad c: Basic you 're basically just merging those for just the sake of endpoint decision ? professor b: if we Yes . professor b: So if well it may be more than endpoint decisions , so the idea would be that you might wanna merge those three grad e: These three ? professor b: Yeah . grad d: What ex  and so either those is true f or false ? professor b:   well it it i here 's where it gets a little tricky . grad d: So professor b:  from the belief - net point of view it is from another point of view of course it 's interest it 's it 's important to know what it 's selling or servicing and so forth . professor b: That that if any of those things is true then it 's the kind of place that you  grad e:  more likely to enter . grad d: So you just wanna have them all pointing to a summary thing ? professor b: You could , yeah . professor b: That 's the p part of the idea , is grad e:  is is that the object type node ? professor b: I d grad e: So are they the is it the kind of object that sells , fixes , or services things ? professor b: Well , o open up object type and let 's see what its values are . professor b: Oh , well OK first of all it 's not objects , we called them entities , right ? grad e: Yeah . grad e: Yeah , I w I was just gonna commercial action inside where people p professor b: Well couldn't I do let 's do commercial  landmark and grad e: And where was the accessible , yeah . professor b: Well accessible I think is different cuz that 's tempor that that varies temporally , grad e: Yeah . grad c: What would a hotel fall under ? professor b: I would call that a service , but but I don't know . grad c: Well  in terms of entity type ? professor b: Say w w well it 's co I would s a a again for this purpose I think it 's commercial . grad d:  what does the underscore - T at the end of each of those things signify ? grad e:  things . grad a: So we 're deriving  this the this feature of whether the the main action at this place happens inside or outside or what we 're deriving that from what kind of activity is done there ? Couldn't you have it as just a primitive feature of the entity ? professor b: Well you could , that 's a that 's a choice . professor b: So  grad a:  it seems like that 's much more reliable cuz you could have outdoor places that sell things and you know indoor places that do something else professor b: Yeah , the problem with it is that it sort of putting in a feature just for one decision , grad a: and  . professor b: now w we may wind up having to do that this i anyway , this i grad a: OK . professor b: at a mental level that 's what we we 're gonna have to sort out . professor b: So , you know what does this look like , what are what are  intermediate things that are worth computing , what are the features we need in order to make all these decisions grad a:   professor b: and what 's the best way to organize this so that  it 's clean and and consistent and all that sort of stuff . I 'm just thinking about how people , human beings who know about places and places to go and so on would store this and it would probably you wouldn't just sort of remember that they sell stuff and then deduce from that that it must be going on inside or something . grad e: Well I think an entity maybe should be regard as a vector of several possible things , it can either em do s do sell things , fix things , service things , exhibit things , it can be a landmark at the same time as doing these things , grad a:   grad e: it 's not either or mmm certainly a place can be a hotel and a famous site . IE a a castle or can be a landmark a or not accessible , some statue grad a:    which is to get rid get rid of that l long link between who the user and the endpoint . grad e: Could we just move it like this ? professor b: No no , I don't want the link there at all . professor b: Because what we 're gonna want is an intermediate thing which is  the endpoint decisi the endpoint decision based o on the user models , so what we we what we talked about is three separate endpoint decisions , so let 's make a new node grad e: Yeah . grad c: Just as a suggestion maybe you could " save as " to keep your old one nice and clean and so you can mess with this one . The old one was not that not that important , I think but grad c: OK , well , not a big deal then . grad c: Well the Isn't there a " save as " inside of java base ? grad e: But I can just take this grad c: OK . This was user something professor b: Well this was grad e: or professor b:  let 's p put it this let 's do endpoint underbar - U . grad e: end point ? professor b: i endpoint , e end poi this is sa grad e: Ah . professor b: let 's say underbar - U , so that 's the endpoint decision  as seen through the grad c: As related from the user model . So let 's let 's actually yeah so lin you can link that up to the grad e: Should I rename this too ? professor b:  yeah , so that , I guess that 's endpoint  grad e:  It 's underscore - E . Or ? Can I ? Where ? What ? professor b: Oh I d eh I don't know . grad c: I think you have to be in move mode before grad e:  - huh . And  maybe th maybe it 's just one who is the user , I don't know , maybe maybe there 's more . grad e: Well if he 's usi if he 's in a car right now what was that people with Harry drove the car into the cafe professor b: Never mind . Now but the now so so but then the question is  so and and we assume that some of these properties would come indirectly through an ontology , but then we had this third idea of input from the discourse . grad e: Well let 's should we finish this , professor b: Sure , grad e:  but surely the user interests professor b: OK . grad e: yeah , yeah professor b: Well , maybe , I again , I d well , OK , put em in but what we 're gonna wanna do is actually  grad c: Well is grad e: Here this was one of my problems we have the user interest is a is a vector of five hundred values , so  That 's from the user model , grad d: Oh you mean level of interest ? grad e:  -  , no not levels of interest but things you can be interested in . grad d: Oh I see , grad e: Gothic churches versus Baroque townhouses versus grad d: right . So why is it oh it , so it 's like a vector of five hundred one 's or zero 's ? grad e: Yea - n is that grad d: Like for each thing are we are you interested in it or not ? grad e: yeah  I I think grad d: I see . But the other thing you could do is have an and this will give you the flavor of the of what you could have a node that 's that was a measure of the match between the object 's feature , you know , the match between the object the entity , I 'm sorry and the user . professor b: So you could have a k a " fit " node and again that would have to be computed by someone else grad e:   professor b: but  so that  grad e: Just as a mental note  professor b: Yeah , that 's all . And and should we say that this interests eh affects the likelihood of of entering ? professor b: Yeah . " Do I have time to go in and climb all the way to the top of the Koelner Dome or do I just have to " " time to take a picture of the outside ? " grad e: Schedule ? professor b: Right . grad c: It seems like everything in a user model a affects professor b: Well that 's what we don't wanna do , see that se cuz then we get into huge combinatorics and stuff like that grad c: Yeah . professor b: an grad c: Cuz if the , and if the user is tired , the user state , grad d: Well grad c: right , it would affect stuff , but I can't see why e anything w everything in the model wouldn't be professor b: Well , but grad d: Right . professor b: Well , that that 's we can't do that , so we we 're gonna have to grad c: Yeah . professor b: but this is a good discussion , we 're gonna have to somehow figure out  some way to encapsulate that  so if there 's some general notion of for example the  relation to the time to do this to the amount of time the guy has or something like that is is the  compatibility with his current state , so that 's what you 'd have to do , you 'd have to get it down to something which  was itself relatively compact , so it could be compatibility with his current state which would include his money and his time and and his energy grad c: Yeah , just seems like it 'd push the problem back a level . grad c: Yeah , but grad d: No but , it 's more than that , like the the more sort of you break it up like because if you have everything pointing to one node it 's like exponential whereas if you like keep breaking it up more and more it 's not exponential anymore . That 's tha there 's one technical one grad c: Sh - sh yeah , professor b: and the other is it it gets used grad c: S so we 'd basically be doing subgrouping ? Subgrouping , basically into mo grad d: Yeah . But it there 's two advantages , one is the technical one that you don't wind up with such big exponential  CBT 's , grad e: Bhaskara ? professor b: the other is it can be it presumably can be used for multiple decisions . professor b: So that if you have this idea of the compatibility with the requirements of an action to the state of the user one could well imagine that that was u grad d: Right . professor b: not only is it sim is it cleaner to compute it separately but it could be that it 's used in multiple places . professor b: OK , you 've got a signal , a d set of decisions  how do we do this ? grad e: What do I have under user state anyhow cuz I named that already something . grad c: I guess the the question  is It 's hard for me to imagine how everything wouldn't just contribute to user state again . professor b: Oh but the thing is that we   we had some things that  grad e: That don't . professor b: that don't grad e: The user interests and the user who who who the user is are completely apart from the fact whether he is tired broke grad c: Sure , but other I thought though the node we 're creating right now is user compatibility to the current action , right ? professor b: the right grad c: Seems like everything in the user model would contribute to whether or not the user was compatible with something .  the that 's the the issue is  would Even if it was true in some abstract general sense it might not be true in terms of the information we actually had and can make use of . And anyway we 're gonna have to find some way to cl  get this sufficiently simple to make it feasible . grad e: Maybe  if we look at the if we split it up again into sort of  if we look at the  the endpoint again we we said that for each of these things there are certain preconditions so you can only enter a place if you are not too tired to do so and also eh have the money to do so if it costs something so if you can afford it and perform it is preconditions . professor b: W w but that eh viewing it without ent yeah view w with our definition of view it 's free cuz you grad e: And so is approaching .  are there are there large things that you would have to pay to get up close to like ,  never mind , not in the current professor b: No we have to enter the park .  So let me suggest we switch to another one ,  clearly there 's more work to be done on this grad e:   professor b: but I think it 's gonna be more instructive to to think about  other decisions that we need to make in path land . grad c: So you can save this one as and open up the old one , right and and then everything would be clean . professor b: Why , I think it 's worth saving this one but I think I 'd I 'd like to keep this one grad d: Yeah . professor b: cuz I wanna see if if we 're gonna reuse any of this stuff . grad e:  so this might be What next ? professor b: Well you tell me , so in terms of the  planner what 's what 's a good one to do ? grad e: Well let 's th this go there or not I think is a good one . So what makes things more likely that professor b: Well the fir see the first thing is , getting back to thing we left out of the other is the actual discourse . grad e: So professor b: So Keith this is gonna get into your world because  we 're gonna want to know you know , which constructions indicate various of these properties grad a:   professor b: s and so I I don't yet know how to do this , I guess we 're gonna wind up pulling out  discourse properties like we have object properties and we don't know what they are yet . professor b: So that that the Go - there decision will have a node from  discourse , and I guess why don't we just stick a discourse thing up there to be as a placeholder for grad e: We we also had discourse features of course for the endpoint . grad e: and so again re that 's completely correct , we have the user model , the situation model here , we don't have the discourse model here yet . professor b: Well the ontology we sort of said we would pull these various kinds of properties from the ontology like exhibiting , selling , and so forth .  This be specific for second year ?  And and we probably will have  something like a discourse for endpoint . grad e:  ? professor b: It 'll have the EVA values if if we have it . OK just for starters and here discourse  professor b: For Go - there , probably is true and false , let 's say . grad e:  well , I think  we 're looking at the the little data that we have , so people say how do I get to the castle and this usually means they wanna go there . grad e: however people also sometimes say how do I get there in order to find out how to get there without wanting to go there . grad e: because they wanna know where it is but in most cases they probably professor b: Yeah , but that doesn't change the fact that you 're you want these two values . So this is sort of some external thing that takes all the discourse stuff and then says here it 's either yep , yay , A , or nay . OK ? professor b: And they 'll be a y  , a user Go - there and maybe that 's all , I don't know . grad d: Situation Go - there , because it 's whether it 's open or not . grad d:  the that interacts with the  EVA thing if they just wanna view it then it 's fine to go there when it 's closed whereas if they want to  professor b: Right . grad d: so professor b: Right , so that 's that 's where it starts getting to be   essentially more interesting , so what  Bhaskara says which is completely right is if you know that they 're only going to view it then it doesn't matter whether it 's closed or not grad a:   So yeah , that 's what I said just having one situational node may not be enough because this that node by itself wouldn't distinguish professor b: Well i i it can have di various values . grad d: Yeah , see I 'm I 'm thinking that any node that begins with " Go - there " is either gonna be true or false . grad a: Also , that node ,  the Go - there s S node would just be fed by separate ones for grad e:   grad a: you know , there 's different things , the strikes and the professor b: Could be . So so now the other thing that Bhaskara eh pointed out is what this says is that  there sh should be a link , and this is where things are gonna get very messy from the endpoint  decision grad d: I guess the final professor b: maybe the t they 're final re and , I guess the very bottom endpoint decision  to the Go - there node . grad d: Maybe we could  have intermediate node that just the Endpoint and the Go - there S node sort of fed into ? professor b: Could be , yeah . Well the Go - there , actually the Endpoint node could feed feed into the Go - there S That 's right , grad d: Yeah , right . professor b: and again we 'll have to do layout at some point , but something like that .  really important in in the belief worl net world not to have loops grad e: I was just gonna professor b:  grad d: Yes . grad e: How long does it take you to to compute  professor b: No it 's much worse than that . It if i loo it it it it it 's not def i it 's not well defined if you 're there are loops , grad d: It things don't converge , yeah . grad e:  R recursive action ? professor b: you just you have to there are all sorts of ways of breaking it up so that there isn't  OK . grad e:  but this isn't , this is this line is just coming from over here . professor b: Yeah , no it 's not a loop yet , I 'm just saying we we , in no , in grad d: Yeah .  what 's what 's another decision you like ? grad e: OK , these have no parents yet , but I guess that sort of doesn't matter . Right ? professor b: Well , the idea is that you go there , you go comes from something about the user from something about the situation and the  the discourse is is a mystery . grad a:  actually professor b: And then also the discourse endpoint , I I guess endpoint sub - D is if you wanna make it consistent . grad a:  actually is this the the right way to have it where  go there from the user and go there from the situation just sort of don't know about each other but they both feed the go there decision because isn't the ,  professor b: I think so . But that still allows for the possibility of the of the user model affecting our decision about whether a strike is the sort of thing which is going to keep this user away from professor b: Maybe not , a Right . grad a: But  OK I was just thinking I guess maybe I 'm conflating that user node with possible possible asking of the user professor b: Yeah . grad a: you know hey there 's a strike on ,  does that affect whether or not you wanna go or something professor b: Ah . Good point , I don't I don't know how we 're going to t  grad a: or Yeah , so that might not come out of a user model but , you know , directly out of interaction . professor b:  My current idea on that would be that each of these decision nodes has questions associated with it . professor b: And the question wouldn't itself be one of these conditional things grad a: OK . professor b: you know , given that you know there 's a strike do you still wanna go ? grad a: Yeah . professor b: But  if you told him a bunch of stuff , then you would ask him do you wanna go ? grad a:   professor b: Alright , but let me let let 's stay with this a minute grad e: But professor b: because I want to do a little bit of organization . The organization is going to be that  the flavor of what 's going on is going to be that  as we s e sort of going to this detail indeed Keith is going to to worry about the various constructions that people might use grad a:   professor b: and Johno has committed himself to being the parser wizard , grad a: Alright . professor b: so what 's going to happen is that eventually like by the time he graduates , OK  they 'll be some sort of system which is able to take the discourse in context and have outputs that can feed the rest of belief - net . I j wa I I assume everybody knows that , I just wanna you know , get closure that that 'll be the game then , grad a:   professor b: so the semantics that you 'll get out of the discourse will be of values that go into the various discourse - based decision nodes . And now some of those will get fancier like mode of transportation and stuff so it isn't by any means  necessarily a simple thing that you want out . So  if there is an and there is mode of transportation grad e: And it there 's a sort of also a split if you loo if you blow this up and look at it in more detail there 's something that comes from the discourse in terms of what was actually just said what 's the utterance go giving us professor b: Yeah . professor b: Yeah , well that , well , we 'll have to decide  how much of th where that goes .  it could be those are two separate things , it could be that the discourse gadget itself integrates em as which would be my guess that you 'd have to do see in order to do reference and stuff like that  you 've gotta have both the current discourse and the context to say I wanna go back there , grad a:   professor b: wow , what does that mean and  grad e:  -  grad a: Now . So grad e: But is th is this picture that 's emerging here just my wish that you have noticed already for symmetry or is it that we get for each each decision on the very bottom we sort of get the sub - E , sub - D , sub - U and maybe a sub - O " O " for " ontology "  meta node professor b: I don't know . grad e: could be professor b: This is this is getting into the thing I wanna talk about next , grad e: so this professor b: which is s if that 's true  how do we wanna combine those ? O or when it 's true ? grad e: but this eh w wou wou would be nice though that , you know , we only have at most four at the moment  arrows going f to each of the  bottom decisions . grad e: It 's too much ? professor b: Well i i it see i if it 's fou if it 's four things and each of them has four values it turns out to be a big CPT , it 's not s completely impossi  it 's it 's not beyond what the system could solve but it 's probably beyond what we could actually  write down . grad c: is that what that professor b: Yeah ,  it 's and I don't think it 's gonna g e I don't think it 'll get worse than that by the way , so le that 's a that 's a good grad d: Mmm yeah . grad e: But but four didn't we decide that all of these had true or false ? So is it 's four professor b:  for go there , but not f but not for the other one 's three values for endpoint already . grad d: Yeah ,  you need actually three to the five because  well  if if it has four inputs and then it itself has three values grad c: Right . grad e:  for endpoint ? No it 's it 's sh professor b: EV - it 's the EVA . grad d: No it still has three , professor b: Since ta they will still have three . professor b: Each so you 're   from each point of view you 're making the same decision . professor b: So from the point of view of the ob of the entity grad e: Want to view that , yeah yeah . grad e: yeah grad d: This and also , the other places where , like for example consider endpoint view , it has inputs coming from user budget , user thrift professor b: Right . S so we 're we 're gonna have to use some t care in the knowledge engineering to not have this explode . And in fact I think it doesn't in the sense that  Read it , you know actually with the underlying semantics and stuff I think it isn't like you have two hundred and fifty - six different  ways of of thinking about whether this user wants to go to some place . But  What I was gonna suggest next is maybe we wanna work on this a little longer but I do want to also talk about the thing that we started into now of  well it 's all fine to say all these arrows come into the si same place what rule of combination is used there . professor b: how do they affect it ? And belief - nets have their own beliefs about  what are good ways to do that . So is it it 's it 's clearer n clear enough what the issue is , grad d: Right . professor b: right ? So do we wanna switch that now or we wanna do some more of this ? grad e: R basically w we just need to sort of in order to get some closure on this figure out how we 're gonna get this picture sort of  completely messy . professor b: Well , here he here 's one of the things that that I th you sh you no , I don't know how easy it is to do this in the interface but you it would be great if you could actually just display at a given time  all the things that you pick up , you click on " endpoint " , OK and everything else fades grad e:   And I does anybody remember the GUI on this ? grad c:  d I would almost say the other way to do that would be to open u or make you know N - many belief - nets and then open them every time you wanted to look at a different one grad e:   grad c: vers cuz  grad e: It 's probably pretty easy do it to do it in HTML , just grad c: Yeah , but grad e:  grad d: HTML ? grad e: Yeah I have each of these thing each of the end belief - nets be be a page and then you click on the thing and then li consider that it 's respective , professor b: Yeah the well the b grad d: OK . grad e: but professor b: anyway so  it clear that even with this if we put in all the arrows nobody is gonna be able to read the diagram . professor b: Alright , so e we have to figure out some eh eh  basically display hack or something to do this because anyway I I let me consi suggest that 's a s not a first - order consideration , we have two first - order considerations which is what are the  influences A , A , and B how do they get combined mathematically , how do we display them is an issue , but  grad c: I don't , yeah I just don't think this has been designed to support something like that . Yeah , I  , it might soon , if this is gonna be used in a serious way like java base then it might soon be necessary to  start modifying it for our purposes . Yeah , and  I that seems like a perfectly feasible thing to get into , but  we have to know what we want first . OK , so why don't you tell us a little bit about decision nodes and what what the choices might be for these ? grad d: So Ah , sorry . So  recall the basic problem which is that  you have a belief - net and you have like a lot of different nodes all contributing to one node . Right ? So as we discussed specifying this kind of thing is a big pain and it 's so will take a long time to write down because for example if these S have three possibilities each and this has three possibilities then you know you have two hundred and forty - three possibilities which is already a lot of numbers to write down . So what  helps us in our situation is that these all have values in the same set , right ? These are all like saying EV or A , right ? So it 's not just a generalized situation like  basically we wanna just take a combination of we wanna view each of these as experts ea who are each of them is making a decision based on some factors and we wanna sort of combine their decisions and create you know ,  sorta weighted combination . So the problem is to specify the  so the conditional property of this given all those , right ? That 's the way belief - nets are defined , like each node given its parents , right ? So  that 's what we want , we want for example P of  let 's call this guy Y and let 's call these X - one , X - two XN , right . So we want probability that Y equals , you know , for example  E given that these guys are I 'll just refer to this as like X  hat or something ,  the co like all of them ? Given that for example the data says you know , A , V , A , E , or something right ? professor b: Yep . professor b: Alright , so  Is that  I yeah , I just wanna make sure everybody is with us before he goes on . professor b: It 's it 's cl e is is it clear what he wants to compute ? grad a:   So Basically  what we don't wanna do is to for every single combination of E and V and A and every single letter E , s give a number grad a:   What we wanna do is find some principled way of  saying what each of these is and we want it to be a valid probability distribution , so we want it to  add up to one , right ? grad a:  . So what  I guess , what Jerry suggested earlier was basically that we , you know view these guys as voting and we just take the  we essentially take  averages , right ? So for example here two people have voted for A , one has voted for V , and one has voted for E , so we could say that the probabilities are , you know , probability of being E is one over four , because one person voted for E out of four and similarly , probability of so this is probability of E s and then probability of A given all that is  two out of four and probability of V is one out of four . Right ? So that 's step that 's the  yeah that 's the that 's the basic  thing . grad d: Is that all OK ? grad e: And that one outcome , that 's professor b: What ? grad e: it 's X X - one voted for A X - two voted for V grad a:   professor b: S so this assumes symmetry and equal weights and all this sort of things , which may or may not be a good assumption , grad e: That 's the outcome . So we 've assumed equal weights whereas it might turn out that you know , some w be that for example , what the  the actual the  verbal content of what the person said , like what  what might be  somehow more  important than the  grad c: X - one matters more i than X - two or grad d: Right . Sure , so we don't wanna like give them all equal weight so currently we 've been giving them all weight one fourth so we could replace this by  W - one , W - two , W - three , and W - four grad a:  . grad d: right ? And in order for this to be a valid probability distribution for each  X - hat , we just need that the W 's sum to one . So they can be for example , you know you you could have point one , point three , point two , and point four , say . And  grad c: So I jus just to make sure I understand this , so in this case  we would still compute the average ? grad d: You 'd compute the weighted average , so the probability of E would be  grad c: OK , so so it 'd be so in this case the probability that Y equals A would be  W one times grad a: Point three . grad c: or A or let 's see , one full quarter times point one grad d: Not one quarter , grad a: No . grad d: so these numbers have been replaced with point one , point three , point two , and point four . So the next possibility is that  we 've given just a single weight to each expert , right , whereas it might be the case that  in certain situations one of the experts is more  reliable and in certain situations the other expert is more reliable . So the way this is handled is by what 's called a mixture of experts , so what you can have is you augment these diagrams like this so you have a new thing called " H " , OK ? This is a hidden variable . And what this is is it gets its input from X - one , X - two , X - three , and X - four , and what it does is it decides which of the experts is to be trusted in this particular situation . So what 's going on is that  this H node looks at these four values of those guys and it decides in given these values which of these isn't likely to be more reliable or most reliable . So H produces some you know , it produces a number , either one , two , three , or four , in our situation , right ? Now this guy he looks at the value of H say it 's two , and then he just selects the  thing . grad a: So so the function of the thing that comes out of H is very different from the function of the other inputs . grad c: So H passes a vector on to the next node ? grad d: It could . grad c: It could ? A vector of the weights as the se grad d: Yeah , it could grad c: oh . grad d: Sorry ? grad a: Well a vector with three zero 's and one one , right ? grad c: Oh it 's basically to tell the bottom node which one of the situations that it 's in or which one of the weighting systems grad d: Right , so  the way you desc grad c: W I was just , if you wanted to pay attention to more than one you could pass a w a weighting s system though too , couldn't you ? OK . grad a:  Does H have to have another input to tell it alpha , beta , whatever , or is the that 's determined by what the experts are saying , like the type of situ OK .  It it just seems that like without that that outside input that you 've got a situation where , you know , like if if  X - one says no , you know , a low value coming out of X - on or i if X - one says no then ignore X - one , you know ,  that seems like that 'd be weird , grad d: Yeah , well could be things like if X - two and X - three say yes then i ignore X - one also . grad c: Oh The situations that H has , are they built into the net or OK , so they they could either be hand coded or learned or OK . grad c: So you specify one of these things for every one of those possi possible situations .  Well ,  to learn them we need data , where are we gonna get data ? Well  we need data with people intentions , right ? grad a: Right , right . But what 's the data about like , are we able to get these nodes from the data ? grad a: Like how thrifty the user is , or do we have access to that ?   grad d: Yeah , but that 's my question , like how do we  , how do we have data about something like   endpoint sub - E , or endpoint sub  you know s S ? grad c: Well , basically you would say , based on in this dialogue that we have which one of the things that they said eh whether it was the entity relations or whatever was the thing that determined what mode it was , grad d: Mmm . I don't think , well you have a can you bring up the function thing ?  w where is the thing that allows you to sort of grad c: That 's on the added variable , isn't it ? grad d: Oh function properties , is that it ?  , I guess not . And  it so e either it 'll allow us to do everything which I think is unlikely , I think more likely it 'll allow us to do very few of these things and in that case we 'll have to  just write up little things that allow you to  create such CPU 's on your own in the java base format . Yeah , I was assuming that 's what we 'd always do because yeah I was assuming that 's what we 'd always do , it 's Right . Well in terms of java base I think it 's basically what you see is what you get in I don't yeah , I would be surprised if it supports anything more than what we have right here . By the way   just talking about  about that general end of things  is there gonna be data soon from what people say when they 're interacting with the system and so on ? Like , what kind of questions are being given being asked ? Cuz OK . I 'm just wondering , because in terms of , you know ,   w the figure I was thinking about this figure that we talked about , fifty constructions or whatever that 's  that 's a whole lot of constructions and  you know ,  one might be f fairly pleased with getting a really good analysis of five maybe ten in a summer so ,  I know we 're going for sort of a rough and ready .  , I I I I was  I was talking about the , you know , if you wanted to do it really in detail and we don't really need all the detail for what we 're doing right now but anyway in terms of just narrowing that task you know which fifty do I do , I wanna see what people are using , so Well , it will inspire me


professor f: So the what w we h have been doing i they would like us all to read these digits . grad b: OK and the way you do it is you just read the numbers not as  each single , so just like I do it . So we 're gonna try to finish by five so people who want to can go hear Nancy Chang 's talk ,  downstairs . professor f: And you guys are g giving talks on tomorrow and Wednesday lunch times , phd a: Yes . OK so , do y do you know what we 're gonna do ? grad b: I thought two things  we 'll introduce ourselves and what we do . And  we already talked with Andreas , Thilo and David and some lines of code were already written today and almost tested and just gonna say we have  again the recognizer to parser thing where we 're working on and that should be no problem and then that can be sort of developed  as needed when we get enter the tourism domain . phd a: S grad b: and  There one of our diligent workers has to sort of volunteer to look over Tilman 's shoulder while he is changing the grammars to English phd a:   Either we do a syllable concatenating  grammar for the English generation which is sort of starting from scratch and doing it the easy way , or we simply adopt the ah  more in - depth  style that is implemented in the German system and  are then able not only to produce strings but also the syntactic parse  not parse not the syntactic tree that is underneath in the syntactic structure which is the way we decided we were gonna go because A , it 's easier in the beginning phd a:   grad b: and  it does require some some knowledge of of those grammars and and and some ling linguistic background . Johno , are you gonna have some time t to do that  w with these guys ? grad e: Sure . And an Yeah , so I I actually wanna f to find out about it too , but I may not have time to get in . grad b: the the ultimate goal is that before they leave we we can run through the entire system input through output on at least one or two sample things . And  and by virtue of doing that then in this case Johno will have acquired the knowledge of how to extend it . grad b: And  also  Ralf has hooked up with David and you 're gonna continue either all through tonight or tomorrow on whatever to get the er parser interface working . grad b: They are thinning out and thickening out lattices and doing this kind of stuff to see what works best . grad d: Yeah , very much professor f: OK , before before you got put to work ? grad d: Yeah professor f: Great . OK , so that 's Sort of one branch is to get us caught up on what 's going on . Also of course it would be really nice to know what the plans are , in addition to what 's sort of already in code . professor f: and we can d I dunno w w was there  a time when we were set up to do that ? It probably will work better if we do it later in the week , after we actually understand  better what 's going on . professor f: So when do you guys leave ? phd a:  we 're here through Sunday , grad d: Oh phd a: so All through Friday would be fine . professor f: Oh , OK , so OK , So so anyt we 'll find a time later in the week to  get together and talk about your understanding of what SmartKom plans are . professor f: grad b: Should we already set a date for that ? Might be beneficial while we 're all here . I can do earlier in the day on Thursday , or  most of the time on Friday , not all . grad b: Thursday morning sounds fine ? professor f: Wha - but , Johno , phd a:   professor f: what are your constraints ? grad e:  Thursday afternoon doesn't work for me , but grad b: Neither does Thursday morning , no ? grad e:  Thursday morning should be fine . professor f: Eleven ? Eleven on Thursday ? grad e: I was just thinking I w I will have leavened by eleven . grad b: but  David is here and he 's actually knows everything about the SmartKom recognizer . grad b: OK so facing to to what we 've sort of been doing here  well for one thing we 're also using this room to collect data . grad b: no not meeting data but sort of sort ah our version of a wizard experiment such not like the ones in Munich but pretty close to it . grad b: The major difference to the Munich ones is that we do it via the telephone phd a: OK . grad b: even though all the recording is done here and so it 's a sort of a computer call system that gives you tourist information phd a:   and part of that is sort of trying to find out whether people change their linguistic verbal behavior when first thinking they speak to a machine and then to a human . grad b: and we 're setting it up so that we can we hope to implant certain intentions in people . For example  we have first looked at a simple sentence that " How do I get to the Powder - Tower ? " OK so you have the castle of Heidelberg phd a: OK . grad b: and  so What will you parse out of that sentence ? Probably something that we specified in M - three - L , that is @ @ " action go to whatever domain , object whatever Powder - Tower " . grad b: And maybe some model will tell us , some GPS module , in the mobile scenario where the person is at the moment . And  we 've sort of gone through that once before in the Deep Mail project and we noticed that first of all what are I should 've brought some slides , but what our So here 's the tower . And our system led people here , to a point where they were facing a wall in front of the tower . There is no entrance there , but it just happens to be the closest point of the road network to the geometric center Because that 's how the algorithm works . So we took out that part of the road network as a hack and then it found actually the way to the entrance . But what we actually observed in Heidelberg is that most people when they want to go there they actually don't want to enter , because it 's not really interesting . grad b: And so what   a s you s let 's say a simple parse from a s from an utterance won't really give us is what the person actually wants . Does he wanna go there to see it ? Does he wanna go there now ? Later ? How does the person wanna go there ? Is that person more likely to want to walk there ? Walk a scenic route ? and so forth . There are all kinds of decisions that we have identified in terms of getting to places and in terms of finding information about things . And we are constructing and then we 've identified more or less the extra - linguistic parameters that may f play a role . And we also want to look closely on the linguistic information that what we can get from the utterance . That 's part of why we implant these intentions in the data collection to see whether people actually phrase things differently whether they want to enter in order to buy something or whether they just wanna go there to look at it . And  so the idea is to construct   suitable interfaces and a belief - net for a module that actually tries to guess what the underlying intention was . And then enrich or augment the M - three - L structures with what it thought what more it sort of got out of that utterance . So if it can make a good suggestion , " Hey ! " you know , " that person doesn't wanna enter . That person just wants to take a picture , " cuz he just bought film , or " that person wants to enter because he discussed the admission fee before " . Or " that person wants to enter because he wants to buy something and that you usually do inside of buildings " and so forth . These ah these types of  these bits of additional information are going to be embedded into the M - three - L structure in an sort of subfield that we have reserved . If not you know , then that 's also something  that we can't really at least we want to offer the extra information . grad b:  t s Ultimately if you have if you can offer that information , somebody 's gonna s do something with it sooner or later . grad e: What was he saying ? grad b: for example , right now I know the GIS from email is not able to calculate these viewpoints . So that 's a functionality that doesn't exist yet to do that dynamically , phd a:   grad b: but if we can offer it that distinction , maybe somebody will go ahead and implement it . Surely nobody 's gonna go ahead and implement it if it 's never gonna be used , so . What have I forgotten about ? Oh yeah , how we do it , professor f: Well th  grad b: yeah that 's the professor f: No no . I s I see questions on peoples ' faces , so why don't phd a: Oh professor f: let 's let 's Let 's hear phd a: Well the obvious one would be if if you envision this as a module within SmartKom , where exactly would that Sit ? That 's the d grad b:  so far I 've thought of it as sort of adding it onto the modeler knowledge module . grad b: but it could sit anywhere in the attention - recognition  basically this is what attention - recognition literally sort of can phd a: Well it 's supposed to do . phd a: Well f from my understanding of what the people at Phillips were originally trying to do doesn't seem to quite fit into SmartKom currently so what they 're really doing right now is only selecting among the alternatives , the hypotheses that they 're given enriched by the domain knowledge and the  discourse modeler and so on . Is there other other things that cuz we wanna not Pa - pass over any you know , questions or concerns that you have . phd a: Well there 're there 're two levels of of giving an answer and I guess on both levels I don't have any  further questions . phd a:  the the two levels will be as far as I 'm concerned as  standing here for the generation module grad d: Mmm . phd a: and the other is is my understanding of what SmartKom  is supposed to be professor f: Right . phd a: and I I think that fits in perfectly professor f: So well , let me Let me s expand on that a little bit from the point of view of the generation . professor f: So the idea is that we 've actually got this all laid out an and we could show it to you ig  Robert didn't bring it today but there 's a a belief - net which is There 's a first cut at a belief - net that that doesn't it isn't fully  instantiated , and in particular some of the the combination rules and ways of getting the the conditional probabilities aren't there . professor f: There are only three possibilities and the  what one would like is for this  , knowledge modeling module to add which of those it is and give it to the planner . professor f: But ,  th the current design suggests that if it seems to be an important decision and if the belief - net is equivocal so that it doesn't say that one of these is much more probable than the other , then an option is to go back and ask for the information you want . For the debugging we 'll probably just have a a drop - down menu and the while you 're debugging you will just OK . But for a full system , then one might very well formulate a query , phd a:   professor f: give it to the dialogue planner and say this , you know ar are you know you are you planning to enter ? Or whatever it whatever that might be . So that 's under that model then , There would be a   a loop in which this thing would formulate a query , phd a: Yes . professor f: OK so , th that  , We probably won't do this early on , because the current focus is more on the decision making and stuff like that . professor f: But While we 're on the subject I just wanted to give you a sort of head 's up that it could be that some months from now we said " OK we 're now ready to try to close that loop " in terms of querying about some of these decisions . So my suggestion then is that you  look into the currently ongoing discussion about how the action plans are supposed to look like . And they 're currently  Agreeing or or in the process of agreeing on an X M L - ification of  something like a state - transition network of how dialogues would proceed . D did you know this Robert ? grad b:  Michael is doing that , right ? phd a: Well  Marcus Lerkult is actually implementing that stuff and Marcus and Michael together are  leading the discussion there , yeah . professor f: And it may be that that  we should early on make sure that they have the flexibility that we need . But they  Have I understood this right ? They they govern more or less the the dialogue behavior or the action phd a:   grad b: It 's not really what you do with the content of the dialogue but it 's So ,  there is this this this nice interf grad d: No , it 's it 's also a quantrant   grad b: i Is it professor f: So there 's ac so there th the word " action " , OK , is is what 's ambiguous here . professor f: So ,  one thing is there 's an actual planner that tells the person in the tourist domain now , phd a: OK . professor f: per tells the person how to go , " First go here , grad d:   So that 's that form of planning , and action , and a route planner and GIS , all sort of stuff . No , in SmartKom terminology that 's  called a function that 's modeled by a function modeler . That 's simply a functionality that you give data as in a query and then you get back from that mmm , a functioning model  which might be a planner or a VCR or whatever . professor f: I think tha I think it 's not going to I think that 's not going to be good enough . So I think the idea of having a , you know , transition diagram for the grammar of conversations is a good idea . professor f: OK ? And I think that we do hav definitely have to get in on it and find out OK . But I think that  when so , when you get to the tourist domain it 's not just an information retrieval system . professor f: Right ? So this i this is where I think this people are gonna have to think this through a bit more carefully . professor f: So , if it 's only like in in the in the film and T V thing , OK , you can do this . professor f: y y your I d I think the notion of this as a self contained  module you know th the functional module that that interacts with with where the tourism g stuff is going probably is too restrictive . professor f: Now I dunno how much people have thought ahead to the tourist domain in this phd a: Probably not enough ,  an another  more basic point there is that the current  tasks and therefore th the concepts in this ac what 's called the action plan and what 's really the dialogue manager . professor f: Yeah phd a:  is based on slots that have to be filled and the  kind of values in these slots would be fixed things like the a time or a movie title or something like this professor f:   phd a: and I 'm not sure if if complex slots of that type are really  being taken into consideration . phd a: So that 's that 's really something we professor f: Could you could you put a message into the right place to see if we can at least ask that question ? phd a:   phd a:  nothing 's being completely settled there grad b: rea yep phd a: so this is really an ongoing discussion grad b:  -  phd a: and that 's grad b: yeah and  it might actually OK ah also because  again in in Deep Map we have faced and implemented those problems once already phd a:   grad b: And  mmm You don't know OK th I 'll I 'll talk to Michael it 's what I do anyway . Who How far is the  the the M - three - L specification for for the la natural language input gone on the the  I haven't seen anything for the  tourist path domain . grad b: right ?  together with the usual gang ,  Petra and Jan grad d: Mmm . Yeah , there 's a meeting next next week I think grad b: OK because That 's Those are the I think the the true key issues is how does the whatever comes out of the language input pipeline look like and then what the action planner does with it and how that is  specified . I didn't think of the internal working of the  the action planner and the language  the function model as sort of relevant . But  the internal workings of of the whether you know there 're dialogue action planners that work with belief - nets that are action planners that work with you know state automata .  it does matter because it does have to keep track of you we are on part six of r a route that consists of eight steps and so forth phd a: Right . OK , so that  , for example , the i it 's the action planner is going to take some spec and s make some suggestions about what the user should do . What the user says after that is going to be very much caught up with what the action planner told it . professor f: If the If the parser and the language end doesn't know what the person 's been told OK th it 's you 're making your life much more difficult than it has to be . professor f: Right ? So if someone says the best t to  go there is by taxi , let 's say . OK , there 's all sorts of dialogues that won't make any sense which would be just fine . professor f:  phd a: That would b but that I think that that  point has been realized and it 's it 's not really  been defined yet but there 's gonna be some kind of feedback and input from  the action planner into all the analysis modules , telling them what to expect and what the current state of the discourse is . professor f: Yeah , but this is not the st this is not just the state of the discourse . professor f: OK so it z and s  , It 's great if people are already taking that into account . professor f:  I think you 're gonna need We talked about this several times that that the the input end is gonna need a fair amount of feedback from the planning end . phd a:  professor f: In in one of these things which are are much more continuous than the just the dialogue over movies and stuff . phd a: And even on on a more basic level the the action planner actually needs to be able to have  an expressive power that can deal with these structures . And not just  say   the dialogue  will consist of ten possible states and th these states really are fixed in in a certain sense . professor f:  ? phd a: You have to professor f: Would there be any chance of getting the terminology changed so that the dialogue planner was called a " dialogue planner " ? Because there 's this other thing The o There 's this other thing in in the tourist domain which is gonna be a route planner phd a: That 'd be nice . professor f: Huh ? So , s So what would happen if we sent a note saying " Gee we 've talked about this and couldn't we change this  th the whole word ? " I have no idea how complicated these things are . phd a: and I think this is just for historical reasons within  , the preparation phase of the project and not because somebody actually believes it ought to be action planner . So if there is resistance against changing it , that 's just because " Oh , We don't want to change things . professor f: Yeah , but that 's not g eh tha That ha has all the wrong connotations . I think you can't it 's fine for looking up when T you know when the show 's on TV . You go to th but I I I I think it 's really really wrong headed for something that you that has a lot of state , it 's gonna interact co in a complicated way with the  understanding parts . Yeah I think just the the spatial planner and the route planner I showed you once the interac action between them among them in the deep map system professor f: Right . grad b: so a printout of the communication between those two fills up I don't know how many pages phd a:  grad b: and that 's just part of how do I get to one place . Markus phd a: Wh - where 's ? grad b: Is he new in the in the ? phd a: Yeah , he 's he started  I think January . grad b: Is he gonna continue with the old  thing ? phd a: No , no he 's completely gonna rewrite everything . grad b: Yes I was just that 's my next question phd a:  grad b: whether we 're we 're gonna stick to Prolog or not . grad b: OK But I do think the the function modeling concept has a certain makes sense in a in a certain light phd a: Yeah . grad b: because the action planner should not be or the dialogue manager in that case should not  w have to worry about whether it 's interfacing with  something that does route planning in this way or that way phd a:   grad b: and it cant sort of formulate its what it wants in a in a rather a abstract  way , you know f " Find me a good route for this . grad b: It doesn't really have to worry ab how route planner A or how route planner B actually wants it . It 's tricky because one could well imagine I think it will turn out to be the case that  , this thing we 're talking about , th the extended n  knowledge modeler will fill in some parameters about what the person wants . One could well imagine that the next thing that 's trying to fill out the detailed  , route planning , let 's say , will also have questions that it would like to ask the user . You could well imagine you get to a point where it 's got a a choice to make and it just doesn't know something . grad b:  professor f: And a I a a good design would would allow that to happen . professor f: If if you know if if you can't make it happen then you you do your best . phd a: Yeah but that doesn't necessarily contradict  an architecture where there really is a pers a def well - defined interface . But but what it nee but th what the point is the in that case the dialogue manager is sort of event driven . So the dialogue manager may think it 's in a dialogue state of one sort , phd a:   professor f: and this one of these planning modules comes along and says " hey , right now we need to ask a question " . phd a: Sure , professor f: It could be y phd a: ye yeah I I think that 's that 's the  concept that people have , professor f: Yeah , yeah it it phd a: yep . phd a: And and the the underlying idea of course is that there is something like kernel modules with kernel functionality that you can plug  certain applications like tourist information or  the home scenario with  controlling a VCR and so on . So wouldn't That 's an additional reason to have this well - defined interface and keep these things like  tourist information external . phd a: But of course the the more complex grad b: Yeah , there is another philosophical issue that I think you know you can evade phd a: yep . grad b: but , at at least it makes sense to me that sooner or later  a service is gonna come and describe itself to you . and that 's sort of what Srini is working on in in in the DAML  project where  you you find a GIS about that gives you information on Berkeley , phd a: Yeah . grad b: and it 's it 's gonna be there and tell you what it can do and how it wants to do things . and so you can actually interface to such a system without ever having met it before and the function modeler and a self - description of the  external service haggle it out phd a:  . grad b: and you can use the same language core , understanding core to interface with planner - A , planner - B , planner - C and so forth . grad b: Which is , you know ,   utopian completely utopian at the moment , but slowly , you know , getting into the realm of the  contingent . And language input for example , is of course  crucial you know also when you do the sort of deep understanding analysis that we envision .  Then of course , the   , you know what is it poverty of the stimulus , yet the m  the less we get of that the better . and  so we we 're thinking , for example how much syntactic analysis actually happens already in the parser . Yeah , are there currently is  no syntactic analysis but in the next release there will be some . grad d: unless professor f: How 's it grad d: and it 's   you can access this professor f: S so  y we we looked at the e current pattern matching thing .  , So what are what are the plans roughly ? grad d:  it 's to to integrate and syntactic analysis . So then an utter more than one utterance is There  there 's often  pause between it and a segmentation occurs .  professor f: So , the  So the idea is to  have a pa y y a particular grad d: yeah professor f: Do you have a particular parser in mind ? Is it  partic d  have you thought through ? Is it an HPSG parser ? Is it a whatever ? grad d: No no it 's  I think it 's it 's totally complicated for it 's just one one person professor f: OK . grad d: and so I have to keep the professor f: Oh , you have to do it . grad d: ah and so things must be simpler professor f: I see , grad d: but  , Miel syntactic analysis with  finite state transducers . grad d: Yeah ,  the problem is th that it has to be very fast because  if you want to for more than one path anywhere professor f: OK . grad d: what 's in the latches from the speech recognizer professor f:   So ,  So there was a chunk parser in Verbmobil , that was one of the  branchers . And I know one of them was a chunk parser and I don't remember who did that . grad b: A Alan ? grad d: I think it 's that might , at Tuebingen I thought . grad d: was Do you know something about that ? phd a: Tubingen was at least involved in putting the chunks together grad d: In Tub - at phd a: I can't quite recall whether they actually produced the chunks in the first place . phd a: Or wh grad d: Oh from from Stuttgart , professor f: There w That 's right . They w They had There were This was done with a two phase thing , where the chunk parser itself was pretty stupid grad d: yeah , also professor f: and then there was a kind of trying to fit them together that h used more context . Yeah professor f: Right ? phd a: Well you s and and especially you did some some  , l  was a learning - based approach which learned from a big corpus of of trees . phd a: And yes the it the chunk parser was a finite - state machine that  Mark Light originally w worked on in while he was in Tuebingen professor f: Right . professor f: But is that the kind of thing y It sounds like the kind of thing that you were thinking of . yeah that 's In this direction , yes professor f: What ? grad d: Yeah , it 's in in this direction . grad b: From Michael Strube , I 've heard very good stuff about the chunk parser that is done by FORWISS , which is in embassy doing the parsing . grad b: So this is sort of came as a surprise to me that you know , embassy s is featuring a nice parser but it 's what I hear . grad b: And they 're doing chunk parsing and it 's  I I can give you the names of the people who do it there . But But  given th the constraints , that you want it to be small and fast and so forth , my guess is you 're probably into some kind of chunk parsing . And  I 'm not a big believer in this  statistical you know , cleaning up  It That seems to me kind of a last resort if  you can't do it any other way . professor f: There is this this one that they did at SRI some years ago Fastus ? grad d:  professor f: a grad d: yeah , I 've I 've looked at it but but it 's no not much  information available . I found , professor f: ah ! grad d: but it 's also finite - state transducers , I thought . grad d: and professor f: And of course it was English oriented , grad d: Yeah , and and Purely finite - state transducers are not so good for German since there 's  professor f:  w Right . grad d: The word order is is  not fixed professor f: Yeah , I guess that 's the point is is all the morphology and stuff . Also it 's  it 's  Yes ,  the  choice between  this processing and that processing and my template matcher . grad d:  professor f: So what about  Did y like Morfix ? a a e y you 've got stemmers ? Or is that something that grad d: yeah but it 's all in the in the lexicon . So it 's professor f: But did you have that ? grad d: Yeah th the information is available . So , but grad d: So professor f: So y you just connect to the lexicon grad d: Yeah professor f: and  at least for German you have all all of the  the stemming information . grad d: It  professor f: Did we look at the German ? I don't remember . professor f: So w wha phd a: n Well I think I think there 's some misunderstanding here professor f: i phd a: it 's Morphix is not used on - line . phd a: s so the lexicon might be derived by Morphix grad d: What ? phd a: but What what 's happening on - line is just   a a retrieval from the lexicon which would give all the stemming information professor f: Right . professor f: What  I didn't reme grad b: We threw out all the forms professor f: Huh ? grad b: because , you know , English , well professor f: Oh OK , so it yeah , s s I thought I 'd grad d:   professor f: So in German then you actually do case matching and things like in the in the pattern matcher or not ? grad d:  Not yet but it 's planned to do that . grad d: Yeah professor f: Have we looked at the German ? Oh , I haven yeah that 's getting it from the lexicon is just fine . In terms of if you 're trying to build some fast parser and so forth and You really might wanna do it in a significantly different way . So you 've you guys have looked at this ? also ? in terms of You know , w if you 're doing this for English as well as German  Do you think now that it would be this doing it similarly ? grad d:  Yeah , it 's  I think it 's  yes , it 's it 's  possible to to do list processing . grad b: Well there 's m I 'm sure there 's gonna be more discussion on that after your talk . grad d:  -  , grad b: We 're just gonna foreshadow what we saw that grad d: yeah . grad b: and  professor f: Now actually ,  Are you guys free at five ? Or Do you have to go somewhere at five o ' clock tonight ? W in ten minutes ? grad d: Ah phd a:   I think we 're expect grad d: mmm . professor f: That 's good , because that will  tell you a fair amount about The form of semantic construction grammar that we 're using . professor f: so So I th I think that probably as good an introduction as you 'll get . professor f: It won't talk particularly about how that relates to what  Robert was saying at the beginning . So we talked about the fact that There 're going to be a certain number of decisions That you want the knowledge modeler to make , that will be then fed to the function module , that does  , route planning . And then one half of this we talked about at little bit is how if you had the right information , if you knew something about what was said and about th the something about was the agent a tourist or a native or a business person or  young or old , whatever . That information , and also about the  , what we 're calling " the entity " , Is it a castle , is it a bank ? Is it a s town square , is it a statue ? Whatever . But the other half of the problem is How would you get that kind of information from the parsed input ? So ,  So what you might try to do is just build more templates , saying  we 're trying to build a templ you know build a template that w  somehow would capture the fact that he wants to take a picture . But  from our point of view this is also a research project and there are a couple of people not here for various reasons who are doing doctoral dissertations on this , phd a:   professor f: and the idea that we 're really after is a very deep semantics based on cognitive linguistics and the notion that there are a relatively small number of primitive conceptual schemas that characterize a lot of activity . But also , importantly for Lakoff and these guys is all sorts of metaphorical things are also characterized this way . professor f: and so s So , what we 're really trying to do is to map from the discourse to the conceptual semantics level . professor f: So another one of these primitive , what are called " image schemas " , is  goal seeking . professor f: And that all sorts of things , particularly in the tourist domain , can be represented in terms of  source , path and goal . So the idea would be could we build an analyser that would take an utterance and say " Aha ! th this utterance is talking about an attempt to reach a goal . The goal is this , the pers the ,  traveller is that ,  the sor w where we are at now is is this , they 've mentioned possible obstacles , et cetera . So if you can do this , then the notion would be that across a very large range of domains , you could use this deep conceptual basis as the interface . professor f: And then ,  The processing of that , both on the input end , recognizing that certain words in a language talk about containers or goals , et cetera , and on the output end , given this kind of information , you can then  make decisions about what actions to take . professor f: And Nancy is going to Her talk is going to be not about using this in applications , but about modeling how children might learn this kind of  deep semantic grammar . Would it be highly ambiguous if and then there would be another module that takes that  highly underspecified deep semantic construction and map it onto the current context to find out what the person really was talking about in that context . or or a professor f: Well that 's that 's that 's where the belief - net comes in . professor f: So part of what you 'll get out of this will be the fact tha w if it works right , OK , that this is an agent that wants to go to this place and that 's their goal phd a:   professor f: OK , phd a: th professor f: part of it comes from the ontology . professor f: And the idea of the belief - net is it combines the information from the dialogue which comes across in this general way , phd a:   professor f: you know this is a this is a goal seeking behavior , along with specific information from the ontology about the kinds of objects involved phd a: Yeah OK , Yeah , yep yep yep yep professor f: and about the situation about " Is it raining ? " I don't know . professor f: And so th the coupling to the situation comes in this model from , at th at th at the belief - net , combining evidence from the dialogue with the ontology with the situation . professor f: But Nancy isn't gonna talk about that , phd a: Yeah , oh yeah , I see , professor f: just about the  phd a: yeah yeah , really . phd g: Is it i in , then , your place , in five five - A ? phd a: Alright .