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