F: OK ?
F: um What what does not work for me is Thursday afternoon .
F: I can do earlier in the day on Thursday , or um most of the time on Friday ,
F: not all .
B: Thursday morning sounds fine ?
F: Wha - but , Johno ,
A: Mm - hmm .
F: what are your constraints ?
E: um Thursday afternoon doesn't work for me , but B: Neither does Thursday morning ,
B: no ?
E: Uh Thursday morning should be fine .
F: Eleven ?
A: OK .
F: Eleven on Thursday ?
E: I was just thinking I w I will have leavened by eleven .
F: Right . Right .
F: This is then out of deference to our non - morning people .
A: Mm - hmm .
A: OK .
A: So at eleven ?
D: Hmm .
A: Thursday around eleven ?
A: OK .
F: Yeah .
F: And actually we can invite um Andreas as well .
B: Uh he will be in Washington , though .
F: Oh that 's true .
F: He 's off off on his trip already .
B: but um David is here and he 's actually knows everything about the SmartKom recognizer .
F: Thilo .
F: OK well yeah
F: maybe we 'll see if David could make it .
F: That would be good .
B: OK so
B: facing to to what we 've sort of been doing here
B: um
B: well for one thing we 're also using this room to collect data .
A: Yeah obviously .
B: um um Not this type of data ,
B: no not meeting data but sort of sort ah our version of a wizard experiment such
A: Oh , OK .
B: not like the ones in Munich but pretty close to it .
A: Mm - hmm .
B: The major difference to the Munich ones is that we do it via the telephone
A: OK .
B: even though all the recording is done here
B: and so it 's a sort of a computer call system that gives you tourist information
A: Mm - hmm .
B: tells you how to get places .
B: And it breaks halfway through the experiment and a human operator comes on .
B: 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 .
A: Yeah .
B: and we 're setting it up so that we can we hope to implant certain intentions in people .
B: For example um we have first looked at a simple sentence that " How do I get to the Powder - Tower ? "
B: OK so you have the castle of Heidelberg
A: OK .
B: and there is a tower and it 's called Powder - Tower .
A: Oh , OK .
A: Yeah .
B: and um so What will you parse out of that sentence ?
B: Probably something that we specified in M - three - L ,
D: Mmm .
B: that is @ @ <comment> " action go to whatever domain , object whatever Powder - Tower " .
B: And maybe some model will tell us , some GPS module , in the mobile scenario where the person is at the moment .
B: And um we 've sort of gone through that once before in the Deep Mail project
B: and we noticed that first of all what are B: I should 've brought some slides ,
B: but what our B: So here 's the tower .
B: Think of this as a two - dimensional representation of the tower .
B: And our system led people here , to a point where they were facing a wall in front of the tower .
B: There is no entrance there , but it just happens to be the closest point of the road network to the geometric center
B: Because that 's how the algorithm works .
B: So we took out that part of the road network as a hack
B: and then it found actually the way to the entrance .
B: which was now the closest point of the road network to
A: Yeah .
B: OK , geometric center .
B: But what we actually observed in Heidelberg is that most people when they want to go there they actually don't want to enter ,
B: because it 's not really interesting .
B: They wanna go to a completely different point where they can look at it and take a picture .
A: Oh , OK .
D: Hmm .
A: Yeah .
B: And so what uh uh a s you s
B: let 's say a simple parse from a s from an utterance won't really give us is what the person actually wants .
B: Does he wanna go there to see it ?
B: Does he wanna go there now ?
B: Later ?
B: How does the person wanna go there ?
B: Is that person more likely to want to walk there ?
B: Walk a scenic route ?
B: and so forth .
B: There are all kinds of decisions that we have identified in terms of getting to places and in terms of finding information about things .
B: And we are constructing and then we 've identified more or less the extra - linguistic parameters that may f play a role .
B: Information related to the user and information related to the situation .
B: And we also want to look closely on the linguistic information that
B: what we can get from the utterance .
B: That 's part of why we implant these intentions in the data collection to see whether people actually phrase things differently
B: whether they want to enter in order to buy something or whether they just wanna go there to look at it .
B: And um so the idea is to construct uh um suitable interfaces and a belief - net for a module that actually tries to guess what the underlying intention was .
B: And then enrich or augment the M - three - L structures with what it thought what more it sort of got out of that utterance .
B: So if it can make a good suggestion , " Hey ! "
B: you know , " that person doesn't wanna enter .
B: That person just wants to take a picture , " cuz he just bought film ,
B: or " that person wants to enter because he discussed the admission fee before " .
B: Or " that person wants to enter because he wants to buy something
B: and that you usually do inside of buildings " and so forth .
B: These ah these types of uh 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 .
B: And if the action planner does something with it , great .
B: If not you know , then that 's also something um that we can't really B: at least we <comment> want to offer the extra information . We don't really um we 're not too worried .
A: Mm - hmm .
D: Hmm .
B: I mean t s Ultimately if you have if you can offer that information , somebody 's gonna s do something with it sooner or later .
B: That 's sort of part of our belief .
E: What was he saying ?
B: Um , for example , right now I know the GIS from email is not able to calculate these viewpoints .
B: So that 's a functionality that doesn't exist yet to do that dynamically ,
A: Mm - hmm .
B: but if we can offer it that distinction , maybe somebody will go ahead and implement it .
B: Surely nobody 's gonna go ahead and implement it if it 's never gonna be used ,
B: so .
B: What have I forgotten about ?
B: Oh yeah ,
F: Well th uh
B: how we do it ,
B: yeah that 's the
F: No no .
F: It 's a good time to pause .
F: I s I see questions on peoples ' faces ,
F: so why don't F: let 's let 's Let 's hear A: Oh
A: Well the obvious one would be if if you envision this as a module within SmartKom , where exactly would that Sit ?
B: um so far I 've thought of it as sort of adding it onto the modeler knowledge module .
A: That 's the d
D: Hmm .
A: OK , yeah .
B: So this is one that already adds additional information to the
A: Makes perfect sense .
A: Yes .
D: Hmm , ah .
B: but it could sit anywhere in the attention - recognition
B: I mean basically this is what attention - recognition literally sort of can A: Well it 's supposed to do .
D: Mmm .
A: Yeah
F: That 's what it should do .
F: Right ,
F: yeah .
A: Yeah .
A: Yeah .
D: Huh .
B: Yeah .
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
A: 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 um discourse modeler and so on .
B: Yeah .
B: Yeah .
A: So if if this is additional information that could be merged in by them .
A: And then it would be available to action planning and and others .
B: Yeah .
B: the F: let 's let 's
F: That w
F: OK that was one question .
F: Is there other other things that
F: cuz we wanna not Pa - pass over any you know , questions or concerns that you have .
A: Well there 're there 're two levels of of giving an answer and I guess on both levels I don't have any um further questions .
D: Mmm .
D: Mmm .
A: uh the the two levels will be as far as I 'm concerned as uh standing here for the generation module
D: Mmm .
A: and the other is is my understanding of what SmartKom uh is supposed to be
F: Right .
A: and I I think that fits in perfectly
F: So well , let me Let me s expand on that a little bit from the point of view of the generation .
D: Hmm .
A: Yeah .
F: So the idea is that we 've actually got this all laid out an and we could show it to you ig
F: um Robert didn't bring it today but there 's a a belief - net which is F: There 's a first cut at a belief - net that that doesn't it isn't fully uh instantiated ,
F: and in particular some of the the combination rules and ways of getting the the conditional probabilities aren't there .
F: But we believe that we have laid out the fundamental decisions in this little space
A: Mm - hmm .
F: and the things that influence them .
F: So one of the decisions is what we call this AVE thing .
F: Do you want to um access , view or enter a thing .
D: Hmm .
F: So that 's a a discrete decision .
A: Mm - hmm .
F: There are only three possibilities and the uh F: what one would like is for this uh , knowledge modeling module to add which of those it is and give it to the planner .
A: Mm - hmm .
F: But , uh 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 .
A: Mm - hmm .
F: Alright ?
F: Now there are two ways one can go a imagine doing that .
F: For the debugging we 'll probably just have a a drop - down menu
F: and the while you 're debugging you will just F: OK .
F: But for a full system , then one might very well formulate a query ,
F: give it to the dialogue planner and say this , you know ar are you know you are you planning to enter ?
A: Mm - hmm .
F: Or whatever it whatever that might be .
F: So that 's under that model
F: then , There would be a uh um a loop in which this thing would formulate a query ,
A: Yes .
F: presumably give it to you .
F: That would get expressed and then hopefully you know , you 'd get an answer back .
A: Yep .
F: And that would of course the answer would have to be parsed .
D: Mmm .
F: right
D: Yep .
F: and F: OK so , th that uh ,
A: Yes .
F: We probably won't do this early on , because the current focus is more on the decision making and stuff like that .
A: Yep .
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 .
A: Mm - hmm .
A: Mm - hmm .
D: Hmm .
A: Yep .
A: So my suggestion then is that you um look into the currently ongoing discussion about how the action plans are supposed to look like .
A: And they 're currently um Agreeing or or in the process of agreeing on an X M L - ification of um something like a state - transition network of how dialogues would proceed .
A: and The these um transition networks uh will be what the action planner interprets in a sense .
F: Hmm .
F: D did you know this Robert ?
B: uh Michael is doing that ,
B: right ?
A: Well uh Marcus Lerkult is actually implementing that stuff
A: and Marcus and Michael together are um leading the discussion there ,
B: OK .
A: yeah .
F: So we ha we have to get in on that .
B: Mm - hmm .
A: Yep .
F: because um partly those are like X - schemas .
D: Mmm .
A: Definitely .
F: the transition diagrams .
B: Hmm .
F: And it may be that that um we should early on make sure that they have the flexibility that we need .
B: Hmm .
B: But they uh
B: Have I understood this right ?
B: They they govern more or less the the dialogue behavior or the action A: Mm - hmm .
B: It 's not really what you do with the content of the dialogue but it 's
B: So , I mean there is this this this nice interf
D: uh , No ,
D: it 's it 's also a quantrant uh uh B: i Is it F: So there 's ac so there th the word " action " , OK , is is what 's ambiguous here .
D: I think .
D: Hmm .
A: Yes .
F: So , um one thing is there 's an actual planner that tells the person in the tourist domain now ,
F: per tells the person how to go ,
A: OK .
F: " First go here ,
F: first go there
D: Mm - hmm .
F: uh , you know , take a bus " ,
F: whatever it is .
F: So that 's that form of planning , and action , and a route planner and GIS , all sort of stuff .
F: uh But I think that isn't what you mean .
A: No .
A: No , in SmartKom terminology that 's um called a function that 's modeled by a function modeler .
A: And it 's th that 's completely um encapsulated from th the dialogue system .
A: That 's simply a functionality that you give data as in a query
A: and then you get back from that mmm , a functioning model um which might be a planner or a VCR or whatever .
A: um some result and that 's then then used .
F: Well , OK ,
F: so that 's what I thought .
F: So action he action here means dia uh speech ac uh you know dialogue act .
A: Yeah , yeah .
B: Mmm .
A: Yeah , in that in that sense
A: yes ,
F: Yeah .
A: dialogue act ,
A: yeah .
F: Um , I think tha I think it 's not going to I think that 's not going to be good enough .
F: I I don what uh what I meant by that .
F: So I think the idea of having a , you know , transition diagram for the grammar of conversations is a good idea .
A: Mm - hmm .
F: OK ?
F: And I think that we do hav definitely have to get in on it and find out OK .
F: But I think that um
F: when so , when you get to the tourist domain it 's not just an information retrieval system .
A: Mm - hmm .
F: Right ?
A: Clearly .
F: So this i this is where I think this A: Yes .
F: people are gonna have to think this through a bit more carefully .
A: Mm - hmm .
F: So , if it 's only like in in the in the film and T V thing ,
F: OK , you can do this .
F: And you just get information and give it to people .
F: But what happens when you actually get them moving and so forth and so on
A: Yep .
F: Uh , y y your I d I think the notion of this as a self contained uh module
F: you know th the functional module that that interacts with with where the tourism g stuff is going <comment> probably is too restrictive .
A: Yep .
F: Now I dunno how much people have thought ahead to the tourist domain in this
A: Probably not enough ,
A: I mean an another uh more basic point there is that the current um tasks and therefore th the concepts in this ac
A: what 's called the action plan and what 's really the dialogue manager .
F: Yeah
A: um is based on slots that have to be filled
A: and the um kind of values in these slots would be fixed things like the a time or a movie title or something like this
F: Mm - hmm .
F: Right .
A: whereas in the a um tourist domain it might be an entire route .
F: Indeed .
A: Set - based , or even very complex structured information in these slots
F: Right .
A: and I 'm not sure if if complex slots of that type are really um being taken into consideration .
F: OK .
F: Could you could you put a message into the right place to see if we can at least ask that question ?
A: So that 's that 's really something we
B: Yep .
A: Mm - hmm .
B: rea
A: I mean nothing 's being completely settled there
B: yep
A: so this is really an ongoing discussion
B: Mm - hmm
B: yeah and um it might actually
A: and that 's
B: OK
B: ah also because um again in in Deep Map we have faced and implemented those problems once already
B: maybe we can even shuffle some know how from there to to Markus and Michael .
A: Mm - hmm .
A: Yes .
D: Mmm .
A: Yep .
B: And um mmm You don't know B: OK th
B: I 'll I 'll talk to Michael
B: it 's what I do anyway .
B: Who How far is the uh the the M - three - L specification for for the la natural language input gone on the the uh
B: I haven't seen anything for the uh tourist path domain .
D: Yeah , it 's it 's not defined yet .
B: And um you are probably also involved in that ,
D: Um Yeah .
B: right ?
B: uh together with the usual gang ,
B: um Petra and Jan
D: Mmm .
D: Yeah , there 's a meeting next next week I think
B: OK because That 's Those are the B: I think the the true key issues is how does the whatever comes out of the language input pipeline look like
B: and then what the action planner does with it B: and how that is uh specified .
B: I didn't think of the internal working of the uh the action planner and the language uh the function model as sort of relevant .
B: Because what what they take is sort of this this fixed representation of a of an intention .
B: And that can be as detailed or as crude as you want it to be .
A: Mm - hmm .
B: But um the internal workings of of the B: whether you know there 're dialogue action planners that work with belief - nets that are action planners that work with you know state automata .
B: So that shouldn't really matter too much .
B: I mean it does matter
B: because it does have to keep track of you B: we are on part six of r a route that consists of eight steps and so forth
F: Yeah , th there there I think there are a lot of reasons why it matters .
A: Right .
F: OK ,
F: so that
F: uh ,
F: 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 .
F: What the user says after that is going to be very much caught up with what the action planner told it .
A: Yes .
F: If the If the parser and the language end doesn't know what the person 's been told
F: OK
F: th it 's you 're making your life much more difficult than it has to be .
B: Yeah .
F: Right ?
F: So if someone says the best t to uh go there is by taxi ,
F: let 's say .
F: Now the planner comes out and says you wanna get there fast ,
F: take a taxi .
F: OK .
F: And the language end doesn't know that .
F: OK , there 's all sorts of dialogues that won't make any sense which would be just fine .
A: hmm
F: uh
B: Yeah .
A: That would
A: b but that I think that that uh point has been realized
A: and it 's it 's not really um been defined yet
A: but there 's gonna be some kind of feedback and input from uh the action planner into all the analysis modules , telling them what to expect and what the current state of the discourse is .
D: Mmm .
A: Beyond what 's currently being implemented which is just word lists .
F: Yeah , but this is not the st this is not just the state of the discourse .
B: Mm - hmm .
A: Of of special interest .
F: This is actually the state of the plan .
F: That 's why
B: Mm - hmm .
A: Yes , Yes , Mm - hmm yeah .
F: OK so it z
F: and s
F: uh , It 's great if people are already taking that into account .
F: But One would have t have to see see the details .
A: The specifics aren't really there yet .
A: Yes .
F: Yeah .
A: So , there 's work to do there .
F: So anyway , Robert , that 's why I was thinking that
B: Mm - hmm .
F: um
F: I think you 're gonna need F: We talked about this several times that that the the input end is gonna need a fair amount of feedback from the planning end .
A: hmm
F: In in one of these things which are are much more continuous than the just the dialogue over movies and stuff .