Grad A: Hmm .
Grad C: so , to avoid some confusion .
Grad B: That will be Reuter ? Oh , OK .
Grad C: Yeah .
Grad B: So my scientific director of the EML is also the dean of the International University , one of his many occupations that just contributes to the fact that he is very occupied . And , um , the um , he @ @ might tell us a little bit about what he 's actually doing , and why it is s somewhat related , and by uh using maybe some of the same technologies that we are using . And um . Was that enough of an update ?
Professor F: I think so .
Grad B: In what order shall we proceed ?
Grad D: OK .
Grad B: Maybe you have your on - line Grad D: Uh , yeah , sure . Um , so , I 've be just been looking at , um , Ack ! What are you doing ? Yeah . OK . Um , I 've been looking at the PRM stuff . Um , so , this is , sort of like the latest thing I have on it , and I sorta constructed a couple of classes . Like , a user class , a site class , and and you know , a time , a route , and then and a query class . And I tried to simplify it down a little bit , so that I can actually um , look at it more . It 's the same paper that I gave to Jerry last time . Um , so basically I took out a lot of stuff , a lot of the decision nodes , and then tried to The red lines on the , um , graph are the um , relations between the different um , classes . Like , a user has like , a query , and then , also has , you know um , reference slots to its preferences , um , the special needs and , you know , money , and the user interest . And so this is more or less similar to the flat Bayes - net that I have , you know , with the input nodes and all that . And So I tried to construct the dependency models , and a lot of these stuff I got from the flat Bayes - net , and what they depend on , and it turns out , you know , the CPT 's are really big , if I do that , so I tried to see how I can do , um put in the computational nodes in between . And what that would look like in a PRM . And so I ended up making several classes Actually , you know , a class of with different attributes that are the intermediate nodes , and one of them is like , time affordability money affordability , site availability , and the travel compatibility . And so some of these classes are s some of these attributes only depend on stuff from , say , the user , or s f just from , I don't know , like the site . S like , um , these here , it 's only like , user , but , if you look at travel compatibility for each of these factors , you need to look at a pair of , you know , what the um , preference of the user is versus , you know , what type of an event it is , or you know , which form of transportation the user has and whether , you know , the onsite parking matters to the user , in that case . And that makes the scenario a little different in a PRM , because , um , then you have one - user objects and potentially you can have many different sites in in mind . And so for each of the site you 'll come up with this rating , of travel compatibility . And , they all depend on the same users , but different sites , and that makes a I 'm tr I w I wa have been trying to see whether the PRM would make it more efficient if we do inferencing like that . And so , I guess you end up having fewer number of nodes than in a flat Bayes - net , cuz otherwise you would c well , it 's probably the same . But um , No , you would definitely have be able to re - use , like , um , all the user stuff , and not not having to recompute a lot of the stuff , because it 's all from the user side . So if you changed sites , you you can , you know , save some work on that . But , you know , in the case where , it depends on both the user and the site , then I 'm still having a hard time trying to see how um , using the PRM will help . Um , so anyhow , using those intermediate nodes then , this this would be the class that represent the intermediate nodes . And that would basically it 's just another class in the model , with , you know , references to the user and the site and the time . And then , after you group them together this no the dependencies would of the queries would be reduced to this . And so , you know , it 's easier to specify the CPT and all . Um , so I think that 's about as far as I 've gone on the PRM stuff .
Professor F: Well
Grad D: Right .
Professor F: No . So y you didn't yet tell us what the output is .
Grad D: The output .
Professor F: So what decisions does this make ?
Grad D: OK . So it only makes two decisions , in this model . And one is basically how desirable a site is meaning , um , how good it matches the needs of a user . And the other is the mode of the visit , whether th It 's the EVA decision . Um , so , instead of um , doing a lot of , you know , computation about , you know , which one site it wants of the user wants to visit , I 'll come well , try to come up with like , sort of a list of sites . And for each site , you know , where h how how well it fits , and basically a rating of how well it fits and what to do with it . So . Anything else I missed ?
Professor F: So that was pretty quick . She 's ac uh uh Eva 's got a little write - up on it that uh , probably gives the the details to anybody who needs them . Um , so the You you didn't look at all yet to see if there 's anybody has a implementation .
Grad D: No , not yet , um Professor F: OK . So one so one of the questions , you know , about these P R Ms is
Grad D: Mm - hmm .
Professor F: uh , we aren't gonna build our own interpreter , so if if we can't find one , then we uh , go off and do something else and wait until s one appears . Uh , so one of the things that Eva 's gonna do over the next few weeks is see if we can track that down . Uh , the people at Stanford write papers as if they had one , but , um , we 'll see . So w Anyway . So that 's a a major open issue . If there is an interpreter , it looks like you know , what Eva 's got should run and we should be able to actually um , try to solve , you know , the problems , to actually take the data , and do it . Uh , and we 'll see . Uh , I actually think it is cleaner , and the ability to instantiate , you know , instance of people and sites and stuff , um , will help in the expression . Whether the inference gets any faster or not I don't know . Uh , it wouldn't surprise me if it if it doesn't .
Grad D: Mm - hmm .
Professor F: You know , it 's the same kind of information . I think there are things that you can express this way which you can't express in a normal belief - net , uh , without going to some incredible hacking of sort of rebuilding it on the fly . I mean , the notion of instantiating your el elements from the ontology and stuff fits this very nicely and doesn't fit very well into the extended belief - net . So that was one of the main reasons for doing it . Um . I don't know . So , uh , people who have thought about the problem , like Robert i it looked to me like if {comment} Eva were able to come up with a you know , value for each of a number of uh , sites plus its EVA thing , that a travel planner should be able to take it from there . And you know , with some other information about how much time the person has and whatever , and then plan a route .
Grad B: Um - hmm , um , well , first of all uh , uh , great looks , mu much cleaner , nnn , nnn , Certain certain beauty in it , so , um , if beauty is truth , then , uh we 're in good shape . But , the um , as , uh , mentioned before we probably should look at t the details . So if you have a write - up then uh , I 'd love to read it
Grad D: Mm - hmm .
Grad B: and uh because , um , i Can you go all the way back to the the very top ?
Grad D: Yeah .
Grad B: Um , uh these @ @ {comment} these w w when these are instantiated they take on the same values ? that we had before ?
Grad D: I can't really see the whole thing .
Grad B: or are they have they changed , in a sense ?
Grad D: Well I think I basically leave them to similar things .
Grad B: Uh - huh .
Grad D: Some of the things might that might be different , maybe like are that the hours for the site .
Grad B: Hmm .
Grad D: And , eventually I meant that to mean whether they 're open at this hour or not .
Grad B: Uh - huh .
Grad D: And status would be , you know , more or less like , whether they 're under construction , and and or stuff like that .
Grad B: And the , uh , other question I would have is that presumably , from the way the Stanford people talk about it , you can put the probabilities also on the relations . If Grad D: Which is the structural uncertainty ?
Professor F: Yeah . Yeah , I that 's That I think was actually in the previous the Ubenth stuff . I don't remember whether they carried that over to this or not ,
Grad A: Mmm .
Professor F: uh , structural uncertainty .
Grad B: It 's sort of in the definition or in the in Daphne 's definition of a PRM is that classes and relations ,
Professor F: OK .
Grad B: and you 're gonna have CPT 's over the classes and their relations .
Professor F: Alright .
Grad B: More uncertainty , or or Professor F: Uh ,
Grad B: I should say .
Grad D: I remember them learning when , you know , you don't know the structure for sure ,
Professor F: Yeah .
Grad D: but I don't remember reading how you specify
Grad B: Yeah , that would be exactly my question .
Professor F: Right .
Grad D: wh to start with . Yeah .
Grad B: Well Grad D: Yeah .
Professor F: Yeah . So , uh , the the plan is is when Daphne gets back , we 'll get in touch and supposedly , um , we 'll actually get s deep seriously connected to to their work and
Grad B: Yep .
Professor F: somebody 'll Uh , you know If it 's a group meeting once a week probably someone 'll go down and , whatever . So , we 'll actually figure all this out .
Grad B: OK . OK . Then I think the w long term perspective is is pretty clear . We get rocking and rolling on this again , once we get a package , if , when , and how , then this becomes foregrounded
Grad D: Mm - hmm .
Grad B: profiled , focused , again .
Grad E: Designated ?
Grad A: Of course .
Grad B: And um , until then we 'll come up with a something that 's @ @ {comment} that 's way more complicated for you . Right ?
Grad D: OK .
Grad B: Because this was laughingly easy , right ?
Grad D: Actually I had to take out a lot of the complicated stuff , cuz I I made it really complicated in the beginning , and Jerry was like , " this is just too much " .
Professor F: Yeah . So , um , you could , from this , go on and say suppose there 's a group of people traveling together and you wanted to plan something that somehow , with some Pareto optimal uh , uh , thing for Grad A: That 's good . That 's definitely a job for artificial intelligence .
Professor F: uh , or Grad A: Except for humans can't really solve it either , so .
Grad B: Well that 's not not even something humans yeah .
Professor F: Right . Right . Well that 's the that would that would be a uh , you could sell it , as a Grad A: Yeah .
Professor F: OK , eh you don't have to fight about this , just give your preferences to the Grad A: And then you can blame the computer .
Professor F: w Exactly .
Grad A: So .
Grad B: Hmm . But what does it uh Would a pote potential result be to to split up and never talk to each other again ? You know .
Grad A: That should be one of them .
Grad B: Yeah .
Professor F: Yeah . Right .
Grad E: That 'd be nice .
Grad A: Mmm .
Professor F: Anyway . So . So there i there are some some u uh , you know , uh , elaborations of this that you could try to put in to this structure , but I don't think it 's worth it now . Because we 're gonna see what what else uh what else we 're gonna do . Anyway . But uh , it 's good , yeah and and there were a couple other ideas of of uh , things for Eva to look at in in the interim .
Grad B: Good . Then , we can move on and see what Andreas has got out his sleeve . Or Andy , for that matter ?