fellous@pipiens.usc.edu (Jean-Marc Fellous) (12/17/90)
My general goal (PhD thesis in Computer Science) is to find (if possible ?) models of Emotions to be applied to the different aspects of Computer Science (AI, DAI, Neural Networks, Brain Theory, Logic ...). I would appreciate any suggestion, comment, reference and contacts with people interested in this exiting topic. In the meantime, here is an annoucement - __________________________________________________________________________ / U.S.C \ | | | C N E Student Workshop on Emotions | | | | CALL FOR PAPERS | | ***************** | \__________________________________________________________________________/ The Center For Neural Engineering of the university of Southern California invites all students interested in Emotions to submit a paper to be eventually presented during a one-day Workshop (of a date t.b.a. at the End of February 1991). The Workshop is opened to Graduate students (MA,MS,PhD) and College Seniors irrespective to their major (faculty will only be considered for publication), having pursued (or pursuing) research activities on such aspects of Emotions as - The nature of Emotion - The physiology of Emotion - The perception of Emotions - The relations between Emotion and Cognition - Developemental aspects of Emotion - Artificial Intelligence models of Emotions - Neural network models of Emotions - Philosophical issues of Emotion and reductionism - ... Applicants should send a 2 page summary of the proposed paper and a letter of motivation in which they state their status, major, interests, name, address and telephone number (for reply). Materials should be submitted by January 31st to Jean-Marc Fellous Center for Neural Engineering University of Southern California Los Angeles CA 90089-2520 Telephone (213) 740 3506 email fellous@rana.usc.edu ps: Travel expenses will not be covered by the CNE, but lunch will be provided. pps: Authors of the chosen papers will receive a copy of the presented papers (by mail if they could not attend the Workshop). Thank you, Jean-Marc .
mitchell@tartarus.uchicago.edu (Mitchell Marks) (12/18/90)
>>>>> "JMF" == Jean-Marc Fellous <fellous@pipiens.usc.edu> writes:
JMF> My general goal (PhD thesis in Computer Science) is to find (if
JMF> possible ?) models of Emotions to be applied to the different
JMF> aspects of Computer Science (AI, DAI, Neural Networks, Brain
JMF> Theory, Logic ...). I would appreciate any suggestion, comment,
JMF> reference and contacts with people interested in this exiting
JMF> topic.
I have been reading the book noted in the following reference with
considerable interest. It doesn't fit {\em precisely} what JMF asks
for, but does present a theory of emotion that makes a good fit with
computational-cogsci (AI) ways of thinking.
@book{OCC-emote
,author={Ortony, Andrew and Clore, Gerald L. and Collins, Allan}
,title={The Cognitive Structure of Emotions}
,publisher={Cambridge University Press}
,address={Cambridge and New York}
,year={1988}}
--
Mitch Marks mitchell@cs.UChicago.EDU
...and who am I to argue with my subconscious?PLai@cup.portal.com (Patrick L Faith) (12/18/90)
As to computer emotions ... this is a Very Very important topic ...
and it is not esoteric.
Basicly the connections between states are linear ... i.e. if you
represent something by how it is connected with other things ....
so between each node that are associated there is a connection.
well the problem is this ... how do you effect a group of nodes without
having connections to them. i.e. lets say one node should effect 10000
nodes, it is a major waist of resources to have that one node connected
to each of the effected 10000 nodes. So instead you have non-linear
connections (i.e. not point to point ... or node to node) that effect a
certain type of nodes, or nearby nodes ... etc...
What non-linear connections really are - are emotions. one emotion
node when triggered ... effects all nearby nodes or all nearby nodes
of a certain type.
Thus non-linear connections (emotions) are a very key important concept
in making sophisticated computations, computationally feasable.
sorry for the speed i did these notes ... i don't know of any paper that
treats emotions correctly when doing complicated ai's ... any references
you pick up i'm be interested ... since i'm coding this kind of stuff.
PLaichris@ug.cs.dal.ca (Chris Robertson) (12/31/90)
In article <37025@cup.portal.com> PLai@cup.portal.com (Patrick L Faith) writes: > well the problem is this ... how do you effect a group of nodes without > having connections to them. i.e. lets say one node should effect 10000 > nodes, it is a major waist of resources to have that one node connected > to each of the effected 10000 nodes. So instead you have non-linear > connections (i.e. not point to point ... or node to node) that effect a > certain type of nodes, or nearby nodes ... etc... OK, so you take the "linear connections" out of the MODEL we use to build the program. So let's see, node X has to be involved in the determinations of a whole whack of nodes, say { a1, a2, a3 ... a999, a1000 }. Previously maybe we would keep a list like { <X,a1> <X,a2> .. <X,a1000> } to tell us all of the "connections" outgoing from X, or perhaps the other way with each node having a list of INcoming connections, either way it doesn't matter. So now, if i take you correctly, we just make sure that all of {a1..a1000} can be picked out by the propogation algorithm as belonging to a unique group, lets call it the "a" group. Then we, instead of the linked list mess above, we can just say "X is connected to a*" (however we happen to represent that). So now we have saved resources, right? But as much work must still be done in the PROPOGATION itself, regardless of perhaps a little space saving in the representation. Also, instead of having just a list of internal pointers through which to loop, now we must search all the nodes to see if it is in the "group" currently being stimulated. Even with an index, or storing nodes in a tree, that's still an increasing time complexity, which we STILL must multiply by the number of nodes in the group anyway .. so its a little slower, but probably saves on RAM a bit. Of course, the slight extra time toll could be worth it for thebetter expressive power of the code. I'm sure someone out there has a simulator which lets you define predicates over the node space, then propogate signals according to the truth of logical sentences constructed from them. That would be neat to see. Anybody out there ever seen or written a package like that? | sig > /dev/null ;^) chris@ug.cs.dal.ca
PLai@cup.portal.com (Patrick L Faith) (01/02/91)
I agree with chris R on his point, and find his view interesteing, but I think that the following paragraph might be innacurate: > .... etc ... >But as much work must still be done in the PROPOGATION itself, regardless >of perhaps a little space saving in the representation. Also, instead of >having just a list of internal pointers through which to loop, now we must >search all the nodes to see if it is in the "group" currently being >stimulated. Even with an index, or storing nodes in a tree, that's still >an increasing time complexity, which we STILL must multiply by the number >of nodes in the group anyway .. so its a little slower, but probably >saves on RAM a bit. > ... etc ... I think this is true for sequential processing, but distributed processing works much faster using "emotion" method. For example lets say we have a distributed computer with 32000 nodes/processors on a single bus (in actuallity the bus is multilayered but lets not get into that). When one processor needs to send information to another processor it gains hold of the bus, then sends a message to that single processor. So with direct connections, you need to send that message as many times as you have nodes that are connected. On the other hand, with an "emotion" based scheme, you lock the buss once, and send the message in a broadcast mode, and all of the processors look to see if the message is in their group and process it. Thus, if you need to propogate from one node to 32000 nodes, the "emotion" based method would be around 32000 times faster (not including overhead which would in reality make it around 16000 time faster). PLai ........