B: and
B: there 're ways to uh sort of back off on the purity of your Bayes - net - edness .
A: Mmm .
B: Uh , so . If you co you could ima and I
B: now I don't know that any of those actually apply in this case ,
B: but there is some technology you could try to apply .
A: So it 's possible that we could do something like a summary node of some sort that B: Yeah .
A: OK .
B: Yeah .
B: And , um
B: So .
A: So in that case , the sum we 'd have we A: I mean , these wouldn't be the summary nodes .
A: We 'd have the summary nodes like
A: where the things were I guess maybe if thi if things were related to business or some other B: Yeah .
B: So what I was gonna say is is maybe a good at this point is to try to informally A: Yeah .
B: I mean , not necessarily in th in this meeting , but to try to informally think about what the decision variables are .
B: So , if you have some bottom line uh decision about which mode ,
B: you know , what are the most relevant things .
A: Mmm .
B: And the other trick , which is not a technical trick , it 's kind of a knowledge engineering trick , is to make the n each node sufficiently narrow that you don't get this combinatorics .
B: So that if you decided that you could characterize the decision as a trade - off between three factors , whatever they may be ,
B: OK ?
B: then you could say " Aha , let 's have these three factors " ,
B: OK ?
B: and maybe a binary version f for each , or some relatively compact decision node just above the final one .
A: Mmm .
B: And then the question would be if if those are the things that you care about , uh can you make a relatively compact way of getting from the various inputs to the things you care about .
B: So that y so that , you know , you can sort of try to do a knowledge engineering thing
A: OK .
B: given that we 're not gonna screw with the technology and just always use uh sort of orthodox Bayes - nets , then we have a knowledge engineering little problem of how do we do that .
B: Um
B: and
A: So what I kind of need to do is to take this one and the old one and merge them together ?
B: " Eh - eh - eh . "
B: Yeah .
A: So that B: Well , mmm , something .
B: I mean , so uh , Robert has thought about this problem f for a long time , cuz he 's had these examples kicking around ,
B: so he may have some good intuition about you know , what are the crucial things .
A: Mmm .
B: and , um , I understand where this the uh B: this is a way of playing with this abs Source - path - goal trajector exp uh uh abstraction and and sort of sh displaying it in a particular way .
A: Yeah .
B: Uh , I don't think our friends uh on Wednesday are going to be able to B: Well , maybe they will .
B: Well , let me think about whether whether I think we can present this to them or not .
B: Um ,
B: Uh ,
D: Well , I think this is still , I mean , ad - hoc .
D: This is sort of th the second version and I I I look at this maybe just as a , you know , a a whatever , UML diagram or , you know , as just a uh screen shot , not really as a Bayes - net as John Johno said .
A: We could actually , y yeah draw it in a different way , in the sense that it would make it more abstract .
D: Yeah .
D: But the uh the the nice thing is that you know , it just is a is a visual aid for thinking about these things which has comple clearly have to be specified m more carefully
D: and uh
B: Alright , well , le let me think about this some more ,
B: and uh see if we can find a way to present this to this linguists group that that is helpful to them .
D: I mean , ultimately we we may w w
D: we regard this as sort of an exercise in in thinking about the problem
D: and maybe a first version of uh a module , if you wanna call it that , that you can ask ,
D: that you can give input
D: and it it 'll uh throw the dice for you ,
D: uh throw the die for you ,
D: because um I integrated this into the existing SmartKom system in in the same way as much the same way we can um sort of have this uh this thing .
D: Close this down .
D: So if this is what M - three - L um will look like and what it 'll give us ,
D: um D: And a very simple thing .
D: We have an action that he wants to go from somewhere , which is some type of object , to someplace .
B: Mm - hmm .
D: And this these uh this changed now only um , um D: It 's doing it twice now because it already did it once .
D: Um , we 'll add some action type , which in this case is " Approach " and could be , you know , more refined uh in many ways .
B: Mm - hmm .
B: Good .
D: Or we can uh have something where the uh goal is a public place
D: and it will give us then of course an action type of the type " Enter " .
D: So this is just based on this one um , on this one feature ,
D: and that 's that 's about all you can do .
D: And so in the f
D: if this pla if the object type um here is is a m is a landmark , of course it 'll be um " Vista " .
D: And um this is about as much as we can do if we don't w if we want to avoid uh uh a huge combinatorial explosion where we specify " OK , if it 's this and this but that is not the case " , and so forth , it just gets really really messy .
B: OK , I 'm sorry .
B: You 're you 're D: Hmm ?
B: It was much too quick for me .
B: OK , so let me see if I understand what you 're saying .
B: So , I I do understand that uh you can take the M - three - L and add not and it w
B: and you need to do this , for sure ,
B: we have to add , you know , not too much about um object types and stuff ,
B: and what I think you did is add some rules of the style that are already there that say " If it 's of type " Landmark " , then you take you 're gonna take a picture of it . "
D: Exactly .
B: F full stop ,
B: I mean , that 's what you do .
B: Ev - every landmark you take a picture of ,
D: Every public place you enter , and statue you want to go as near as possible .
B: you enter B: You approach .
B: OK .
B: Uh , and certainly you can add rules like that to the existing SmartKom system .
B: And you just did ,
B: right ?
D: Yeah .
B: OK .
D: And it it would do us no good .
D: That B: Ah .
D: Ultimately .
D: W
B: Well . So , s well , and let 's think about this .
B: Um , that 's a that 's another kind of baseline case ,
B: that 's another sort of thing " OK , here 's a another kind of minimal uh way of tackling this " .
B: Add extra properties ,
B: a deterministic rule for every property
B: you have an action , " pppt ! "
B: You do that .
B: Um , then the question would be
B: Uh Now , if that 's all you 're doing , then you can get the types from the ontology ,
B: OK ?
B: because that 's all you 're all you 're using is this type the types in the ontology and you 're done .
D: Hmm ?
B: Right ?
B: So we don't we don't use the discourse , we don't use the context , we don't do any of those things .
D: No .
B: Alright , but that 's but that 's OK , and I mean it it 's again a kind of one minimal extension of the existing things .
B: And that 's something the uh SmartKom people themselves would B: they 'd say " Sure , that 's no problem B: you know , no problem to add types to the ont "
B: Right ?
D: Yeah .
D: No .
D: And
D: this is just in order to exemplify what what we can do very , very easily is , um
D: we have this this silly uh interface and we have the rules that are as banal as of we just saw , and we have our content .
D: Now , the content B: Hmm .
D: I whi which is sort of what what we see here , which is sort of the Vista , Schema , Source , Path , Goal , whatever .
B: Yeah .
B: Yeah .
D: This will um be um a job to find ways of writing down Image schema , X - schema , constructions , in some some form ,
D: and have this be in a in a in the content ,
D: loosely called " Constructicon " .
D: And the rules we want to throw away completely .
D: And um and here is exactly where what 's gonna be replaced with our Bayes - net ,
D: which is exactly getting the input feeding into here .
D: This decides whether it 's an whether action D: the the Enter ,
D: the Vista ,
D: or the whatever
B: Uh , " approach " , you called it , I think this time .
D: uh Approach um construction should be activated , IE just pasted in .
B: That 's what you said B: Yeah , that 's fine .
B: Yeah , but B: Right .
B: But it 's not construction there , it 's action .
B: Construction is a d is a different story .
D: Yeah .
A: Right .
A: This is uh A: so what we 'd be generating would be a reference to a semantic uh like parameters for the for the X - schema ?
B: For for for B: Yes .
A: OK .
B: Yeah .
B: So that that uh
B: i if you had the generalized " Go " X - schema
B: and you wanted to specialize it to these three ones , then you would have to supply the parameters .
A: Right .
B: And then uh , although we haven't worried about this yet , you might wanna worry about something that would go to the GIS
B: and use that to actually get you know , detailed route planning .
B: So , you know , where do you do take a picture of it and stuff like that .
A: Mm - hmm .
B: But that 's not It 's not the immediate problem .
A: Right .
B: But , presumably that that that functionality 's there when when we A: So the immediate problem is just deciding w which D: Aspects of the X - schema to add .
B: Yeah , so the pro The immediate problem is is back t t to what you were what you are doing with the belief - net .
A: Yeah .
B: You know , uh what are we going to use to make this decision A: Right and then , once we 've made the decision , how do we put that into the content ?
B: Yeah .
B: Right .
B: Right .
B: Well , that that actually is relatively easy in this case .
A: OK .
B: The harder problem is we decide what we want to use , how are we gonna get it ?
B: And that the the that 's the hardest problem .
B: So , the hardest problem is how are you going to get this information from some combination of the what the person says and the context and the ontology .
B: The h
B: So , I think that 's the hardest problem at the moment is is
A: OK .
B: where are you gonna B: how are you gonna g get this information .
B: Um ,
B: and that 's so , getting back to here ,
B: uh , we have a d a technical problem with the belief - nets that we we don't want all the com
A: There 's just too many factors right now .
B: too many factors if we if we allow them to just go combinatorially .
A: Right .
B: So we wanna think about which ones we really care about
B: and what they really most depend on ,
B: and can we c you know , clean this this up to the point where it A: So what we really wanna do i
A: cuz this is really just the three layer net , we wanna b make it expand it out into more layers basically ?
B: Right .
B: We might .
B: Uh , I mean that that 's certainly one thing we can do .
B: Uh , it 's true that the way you have this , a lot of the times you have B: what you 're having is the values rather than the variable .
B: So uh A: Right .
B: OK ?
A: So instead of in instead it should really be just be " intention " as a node instead of " intention business " or " intention tour " .
B: So you B: Yeah ,
B: right ,
B: and then it would have values , uh , " Tour " , " Business " , or uh " Hurried " .
A: Right .
B: But then but i it still some knowledge design to do , about i how do you wanna break this up , what really matters .
A: Right .
B: I mean , it 's fine .
B: You know , we have to it 's it 's iterative .
B: We 're gonna have to work with it some .
A: I think what was going through my mind when I did it was someone could both have a business intention and a touring intention
A: and
A: the probabilities of both of them happening at the same time B: Well , you you could do that .
B: And it 's perfectly OK to uh insist that that , you know , th um , they add up to one ,
B: but that there 's uh that that it doesn't have to be one zero zero .
A: Mmm .
A: OK .
B: OK .
B: So you could have the conditional p
B: So
B: the each of these things is gonna be a a a probability .
B: So whenever there 's a choice ,
B: uh B: so like landmark - ness and usefulness ,
A: Well , see I don't think those would be mutually B: OK A: it seems like something could both be B: Absolutely right .
A: OK .
B: And so that you might want to then have those
B: b Th - Then they may have to be separate .
B: They may not be able to be values of the same variable .
D: Object type ,
D: mm - hmm .
B: So that 's B: but again , this is this is the sort of knowledge design you have to go through .
B: Right .
B: It 's you know , it 's great is is , you know , as one step toward uh toward where we wanna go .
D: Also it strikes me that we we m may want to approach the point where we can sort of try to find a uh , a specification for some interface , here that um takes the normal M - three - L , looks at it .
D: Then we discussed in our pre - edu EDU meeting um how to ask the ontology , what to ask the ontology
D: um the fact that we can pretend we have one , make a dummy until we get the real one ,
D: and so um we we may wanna decide we can do this from here ,
D: but we also could do it um you know if we have a a a belief - net interface .
D: So the belief - net takes as input , a vector ,
D: right ?
D: of stuff .
D: And it D: Yeah .
D: And um it Output is whatever , as well .
D: But this information is just M - three - L ,
D: and then we want to look up some more stuff in the ontology
D: and we want to look up some more stuff in the D: maybe we want to ask the real world ,
D: maybe you want to look something up in the GRS ,
D: but also we definitely want to look up in the dialogue history um some s some stuff .
D: Based on
D: we we have uh D: I was just made some examples from the ontology
D: and so we have for example some information there that the town hall is both a a a building and it has doors and stuff like this ,
D: but it is also an institution , so it has a mayor and so forth and so forth
D: and we get relations out of it and once we have them , we can use that information to look in the dialogue history ,
D: " were any of these things that that are part of the town hall as an institution mentioned ? " ,
B: Mm - hmm .
D: " were any of these that make the town hall a building mentioned ? " ,
C: Right .
D: and so forth ,
D: and maybe draw some inferences on that .
D: So this may be a a sort of a process of two to three steps before we get our vector , that we feed into the belief - net ,
D: and then B: Yeah .
B: I think that 's I think that 's exactly right .
B: There will be rules , but they aren't rules that come to final decisions , they 're rules that gather information for a decision process .
D: Yeah .
B: Yeah ,
B: no I think that 's that 's just fine .
B: Uh , yeah .
B: So they 'll they presumably there 'll be a thread or process or something that
B: " Agent " , yeah , " Agent " , whatever you wan wanna say , yeah ,
B: that uh is rule - driven , and can can uh can do things like that .
B: And um there 's an issue about whether there will be that 'll be the same agent and the one that then goes off and uh carries out the decision ,
B: so it probably will .
B: My guess is it 'll be the same basic agent that um can go off and get information , run it through a a c this belief - net that B: turn a crank in the belief - net ,
B: that 'll come out with s uh more another vector ,
B: OK ,
B: which can then be uh applied at what we would call the simulation or action end .
B: So you now know what you 're gonna do
B: and that may actually involve getting more information .
B: So on once you pull that out , it could be that that says " Ah !
B: Now that we know that we gonna go ask the ontology something else . "
B: OK ?
B: Now that we know that it 's a bus trip ,
B: OK ?
B: we didn't We didn't need to know beforehand , uh how long the bus trip takes or whatever , but but now that we know that 's the way it 's coming out then we gotta go find out more .
D: Mm - hmm .
B: So I think that 's OK .
D: Mm - hmm .
D: So this is actually , s if if we were to build something that is um ,
D: and , uh , I had one more thing ,
D: the it needs to do D: Yeah .
D: I think we I I can come up with a a code for a module that we call the " cognitive dispatcher " , which does nothing ,
B: OK .
D: but it looks of complect object trees and decides how are there parts missing that need to be filled out ,
D: there 's this is maybe something that this module can do , something that this module can do
D: and then collect uh sub - objects and then recombine them and put them together .
D: So maybe this is actually some some useful tool that we can use to rewrite it , and uh get this part ,
B: Oh , OK .
B: Uh .
D: then .
D: Yeah .
B: I confess , I 'm still not completely comfortable with the overall story .
B: Um .
B: I
B: i This this is not a complaint , this is a promise to do more work .
B: So I 'm gonna hafta think about it some more .
B: Um . In particular B: see what we 'd like to do , and and this has been implicit in the discussion , is to do this in such a way that you get a lot of re - use .
B: So . What you 're trying to get out of this deep co cognitive linguistics is the fact that w if you know about source source , paths and goals , and nnn <comment> all this sort of stuff , that a lot of this is the same , for different tasks .
B: And that uh there 's there 's some some important generalities that you 're getting ,
B: so that you don't take each and every one of these tasks and hafta re - do it .
B: And I don't yet see how that goes .
B: Alright .
D: There 're no primitives upon which uh
B: u u What are the primitives , and how do you break this D: yeah .
B: So I y I 'm just just there saying eee <comment> well you I know how to do any individual case ,
B: right ?
B: but I don't yet B: see what 's the really interesting question is can you use uh deep uh cognitive linguistics to get powerful generalizations .
B: And
D: Yep .
B: um
D: Maybe we sho
D: should we a add then the " what 's this ? " domain ?
D: N
D: I mean , we have to
D: " how do I get to X " .
D: Then we also have the " what 's this ? " domain , where we get some slightly different C: Right .
B: Could . Uh .
D: Um Johno , actually , does not allow us to call them " intentions " anymore .
B: Yeah .
D: So he he dislikes the term .
B: Well , I I don't like the term either , so I have n i
B: uh
B: i i y w i i
B: It uh D: But um , I 'm sure the " what 's this ? " questions also create some interesting X - schema aspects .
B: Could be .
D: So .
B: I 'm not a I 'm not op particularly opposed to adding that or any other task ,
B: I mean , eventually we 're gonna want a whole range of them .
D: Mm - hmm .
C: That 's right .
B: Uh ,
B: I 'm just saying that I 'm gonna hafta do some sort of first principles thinking about this .
B: I just at the moment don't know .
D: Mm - hmm .
B: H
B: No .
B: Well , no the Bayes the Bayes - nets B: The Bayes - nets will be dec specific for each decision .
B: But what I 'd like to be able to do is to have the way that you extract properties , that will go into different Bayes - nets , be the uh general .
B: So that if you have sources , you have trajectors and stuff like that ,
B: and there 's a language for talking about trajectors ,
B: you shouldn't have to do that differently
B: for uh uh going to something , than for circling it ,
B: for uh telling someone else how to go there ,
D: Getting out of B: whatever it is .
B: So that that , the the decision processes are gonna be different
B: What you 'd really like of course is the same thing you 'd always like which is that you have um a kind of intermediate representation which looks the same o over a bunch of inputs and a bunch of outputs .