Now can you give me the uh remote T OK so Eva co uh could you Go read and your read numbers Yeah OK Alright Yeah get started Um Hopefully Nancy will come if not she won't Uh Robert do you uh have any way to turn off your uh screensaver on there so that not going off every uh it seems to have about at two minute Yeah I uh not that I didn't try OK and um I I told it to stay on forever and ever but if not plugged in it just doesn't obey my commands It has a mind Got it conserve I just You know sort of keep on wiggling Yeah OK But uh just be m m working on it at intensity so it doesn't happen see Should we plunge right into it Yeah So so you like to So what tried to do here is list all the decision nodes that we have identified on this side Commented and what about and sort of the properties we may um give them And here are the uh tasks to be implemented via our data collection So all of these tasks The reading is out of these tasks more or less imply that the user wants to go there sometime or the other And analogously for example here we have our EVA um intention And these are the data tasks where w we can assume the person would like to enter view or just approach the thing Analogously the same on the object information we can see that you know we have sort of created these tasks before we came up with our decision nodes so a lot of things where we have no analogous tasks and that may or may not be a problem We can change the tasks slightly if we feel that we should have data for e sort of for every decision node so trying to im um implant the intention of going to a place now going to a place later on the same tour or trying to plant the intention of going sometime on the next tour or the next day or whenever right But I So that might be overdoing it Yeah So let me pop up a level And uh s s make sure that all oriented the same So What gonna do today is two related things Uh one of them is to work on the semantics of the which is going to be the main inference engine for thi the system uh making decisions And decisions are going to turn out to be parameter choices for calls on other modules so f the natural language understanding thing is uh we think gonna only have to choose parameters but You know a fairly large set of parameters So to do that we need to do two things One of which is figure out what all the choices are which done a fair amount Then we need to figure out what influences its choices and finally we have to do some technical work on the actual belief relations and presumably estimates of the probabilities and stuff But we aren't gonna do the probability stuff today Technical stuff do uh another day Probably next week But we are gonna worry about all the decisions and the things that pert that contribute to them And also sort of uh in the same process going to work with Fey on what there should be in the dialogues So One of the s steps coming up real soon is to actually get subjects uh in here and have them actually record like this Uh record dialogues more or less And depending on what Fey sort of provokes them to say get information on different things So people phrase different intentions v more yeah less people with the phrase them and so Uh for you know Keith and people worrying about what constructions people use uh we have some i we have some ways to affect that by the way the dialogues go So what Robert kindly did is to lay out a table of the kinds of uh things that that might come up and the kinds of decisions So the uh uh on the left are decision nodes and discreet values So if if right you can get by with um just this middle column worth of decisions and not all that many and perfectly feasible technically to build that will do that And he has a handout Yeah Maybe it was too fast plunging in there because Yeah j we have two updates Um you can look at this if you want these are what our going to have to fill out Any comments I can m still be made and the changes will be put in correspondingly Let me summarize in two sentences mainly for benefit who probably has not heard about the data collection OK at all Or have you heard about it Not that No much you didn't OK We were gonna put this in front of people They give us some information on themselves OK Then then they will read uh a task where lots of German words are sort of thrown in between And um and they have to read isolated proper names And these I change see a release this is not the release form This Got is it the OK speaker fine information form The release form is over there in that box Alright fair enough And um And then they gonna have to f um um choose from one of these tasks which are listed here They they pick a couple say three uh uh six as a matter of fact Six different things they sort of think they would do if they were in Heidelberg or traveling someplace and um and they have a map Hmm Like this Very sketchy simplified map And they can take notes on that map And then they call this computer system that OK works perfectly and understands everything And um The This is comp fictional system obviously Yeah the computer huh system sits right in front of you Yes Fey I understand does everything everything Yes I do And she has a way of making this machine talk So she can copy sentences into a window or type really fast and this machine will use speech synthesis to produce that So if you ask How do I get to the castle then a m s several seconds later come out of here In order to get to the castle you do OK Yeah And um And then after three tasks the system breaks down And Fey comes on the phone as a human operator And says Sorry the system broke down but continue And we sort of get the idea what people do when they s think they speak to a machine and what people say when they think they speak to a human or OK know or assume they speak to a human the data collection And um And Fey has some thirty subjects lined up Something Yeah And more And more