Professor B: Well , here 's the problem . And and uh Bhaskara and I was talking about this a little earlier today is , if we just do this , we could wind up with a huge uh , combinatoric input to the Mode thing . And uh Grad A: Well I oh yeah , I unders I understand that , I just uh it 's hard for me to imagine how he could get around that .
Professor B: Well , i But that 's what we have to do .
Grad A: OK .
Professor B: OK , so , so , uh . There there are a variety of ways of doing it . Uh . Let me just mention something that I don't want to pursue today which is there are technical ways of doing it , uh I I slipped a paper to Bhaskara and about Noisy - OR 's and Noisy - MAXes and there 're ways to uh sort of back off on the purity of your Bayes - net - edness .
Grad A: Mmm .
Professor B: Uh , so . If you co you could ima and I now I don't know that any of those actually apply in this case , but there is some technology you could try to apply .
Grad A: So it 's possible that we could do something like a summary node of some sort that OK .
Professor B: Yeah . Yeah . And , um So .
Grad A: So in that case , the sum we 'd have we I mean , these wouldn't be the summary nodes . We 'd have the summary nodes like where the things were I guess maybe if thi if things were related to business or some other Professor B: Yeah .
Grad A: Yeah .
Professor B: So what I was gonna say is is maybe a good at this point is to try to informally I mean , not necessarily in th in this meeting , but to try to informally think about what the decision variables are . So , if you have some bottom line uh decision about which mode , you know , what are the most relevant things .
Grad A: Mmm .
Professor B: And the other trick , which is not a technical trick , it 's kind of a knowledge engineering trick , is to make the n each node sufficiently narrow that you don't get this combinatorics . So that if you decided that you could characterize the decision as a trade - off between three factors , whatever they may be , OK ? then you could say " Aha , let 's have these three factors " , OK ? and maybe a binary version f for each , or some relatively compact decision node just above the final one .
Grad A: Mmm .
Professor 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 . So that y so that , you know , you can sort of try to do a knowledge engineering thing
Grad A: OK .
Professor B: given that we 're not gonna screw with the technology and just always use uh sort of orthodox Bayes - nets , then we have a knowledge engineering little problem of how do we do that . Um and
Grad A: So what I kind of need to do is to take this one and the old one and merge them together ?
Professor B: " Eh - eh - eh . " Yeah .
Grad A: So that Professor B: Well , mmm , something . I mean , so uh , Robert has thought about this problem f for a long time , cuz he 's had these examples kicking around , so he may have some good intuition about you know , what are the crucial things .
Grad A: Mmm .
Professor B: and , um , I understand where this the uh 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 .
Grad A: Yeah .
Professor B: Uh , I don't think our friends uh on Wednesday are going to be able to Well , maybe they will . Well , let me think about whether whether I think we can present this to them or not . Um , Uh ,
Grad D: Well , I think this is still , I mean , ad - hoc . 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 .
Grad A: We could actually , y yeah draw it in a different way , in the sense that it would make it more abstract .
Grad D: Yeah . 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
Professor B: Alright , well , le let me think about this some more ,
Grad D: and uh
Professor B: and uh see if we can find a way to present this to this linguists group that that is helpful to them .
Grad D: I mean , ultimately we we may w w we regard this as sort of an exercise in in thinking about the problem and maybe a first version of uh a module , if you wanna call it that , that you can ask , that you can give input and it it 'll uh throw the dice for you , uh throw the die for you , 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 . Close this down . So if this is what M - three - L um will look like and what it 'll give us , um And a very simple thing . We have an action that he wants to go from somewhere , which is some type of object , to someplace .
Professor B: Mm - hmm .
Grad D: And this these uh this changed now only um , um It 's doing it twice now because it already did it once . Um , we 'll add some action type , which in this case is " Approach " and could be , you know , more refined uh in many ways .
Professor B: Mm - hmm . Good .
Grad D: Or we can uh have something where the uh goal is a public place and it will give us then of course an action type of the type " Enter " . So this is just based on this one um , on this one feature , and that 's that 's about all you can do . And so in the f if this pla if the object type um here is is a m is a landmark , of course it 'll be um " Vista " . 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 .
Professor B: OK , I 'm sorry . You 're you 're Grad D: Hmm ?
Professor B: It was much too quick for me . OK , so let me see if I understand what you 're saying . So , I I do understand that uh you can take the M - three - L and add not and it w and you need to do this , for sure , we have to add , you know , not too much about um object types and stuff , and what I think you did is add some rules of the style that are already there that say " If it 's of type " Landmark " , then you take you 're gonna take a picture of it . "
Grad D: Exactly .
Professor B: F full stop , I mean , that 's what you do . Ev - every landmark you take a picture of ,
Grad D: Every public place you enter , and statue you want to go as near as possible .
Professor B: you enter You approach . OK . Uh , and certainly you can add rules like that to the existing SmartKom system . And you just did , right ? OK .
Grad D: Yeah . And it it would do us no good .
Professor B: Ah .
Grad D: That Ultimately .
Professor B: Well . So , s well , and let 's think about this .
Grad D: W
Professor B: Um , that 's a that 's another kind of baseline case , that 's another sort of thing " OK , here 's a another kind of minimal uh way of tackling this " . Add extra properties , a deterministic rule for every property you have an action , " pppt ! " You do that . Um , then the question would be Uh Now , if that 's all you 're doing , then you can get the types from the ontology , OK ? because that 's all you 're all you 're using is this type the types in the ontology and you 're done .
Grad D: Hmm ?
Professor B: Right ? So we don't we don't use the discourse , we don't use the context , we don't do any of those things .
Grad D: No .
Professor 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 . And that 's something the uh SmartKom people themselves would they 'd say " Sure , that 's no problem you know , no problem to add types to the ont " Right ?
Grad D: Yeah . No . And this is just in order to exemplify what what we can do very , very easily is , um 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 .
Professor B: Hmm .
Grad D: Now , the content I whi which is sort of what what we see here , which is sort of the Vista , Schema , Source , Path , Goal , whatever .
Professor B: Yeah . Yeah .
Grad D: This will um be um a job to find ways of writing down Image schema , X - schema , constructions , in some some form , and have this be in a in a in the content , loosely called " Constructicon " . And the rules we want to throw away completely . And um and here is exactly where what 's gonna be replaced with our Bayes - net , which is exactly getting the input feeding into here . This decides whether it 's an whether action the the Enter , the Vista , or the whatever
Professor B: Uh , " approach " , you called it , I think this time .
Grad D: uh Approach um construction should be activated , IE just pasted in .
Professor B: That 's what you said Yeah , that 's fine . Yeah , but Right . But it 's not construction there , it 's action . Construction is a d is a different story .
Grad D: Yeah .
Grad A: Right . This is uh so what we 'd be generating would be a reference to a semantic uh like parameters for the for the X - schema ?
Professor B: For for for Yes .
Grad A: OK .
Professor B: Yeah . So that that uh i if you had the generalized " Go " X - schema and you wanted to specialize it to these three ones , then you would have to supply the parameters .
Grad A: Right .
Professor B: And then uh , although we haven't worried about this yet , you might wanna worry about something that would go to the GIS and use that to actually get you know , detailed route planning . So , you know , where do you do take a picture of it and stuff like that .

Summary: The team was concerned that the combinatorial input that would result from the various linguistic and contextual schemas would be enormous for the Bayes net. The Professor suggested that it's best to narrow down the decision variables, perhaps by studying the trade-offs between different input factors. The team thought that perhaps they could keep the kinds of objects in the environment to a small subset and make rules governing actions around those objects.