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1. Teach TIC TAC TOE

Develop an expert player, maybe a production system. And useSelf's scheme of evaluating functions on two different databases. Tic-tac-toe may be too easy to learn, but checkers,for instance is much to hard. A natural language front endwou ld be nice.

2. EPAM as a student model

can you teach people how to memorize a list using epam asa model of his cog-processes, any better than atkinson andpaulson ?

3. How to get to the airport

describe systems for getting there in

formal logicsmantic netsprocedural-conniveractive structural nets

this is to be a real survey of the representaion of meaning,a really hairy paper, i think.

try to aviod phiosophy: "What is the meaning ofmeaning ?"

4. Computet— assisted protocol analysis

read Newell 's work on this: Waterman & Newell, CACM

5. using a fancy (production system) model of the studentin Ralph's calculus course. Predict wrong approaches

6. Mycin, pulminary disease projects with Bruce

J

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7.

EMERGE,

for English MERGE

use a small grammar for sorting expressions to build anatural front end to merge, e.g.

SEMERGEyes. . . .♦insert the last page of goo* after page 4 of foo*-*put page 9 of foos in front of the first page"s-quit

gives: foo 9, foo 1-4, goo

inf,

foo

an even more general idea. it could sort a file in an arbitraryway, like:

alphabetically, by the first line on each pageword on each lineword in a paragraph

specfied order (7-9,3/1-2...) of pages, lines, paragraphs

- reverse order of whatever

meaning of words, lines, paragraphs* etc. could be specified(in terms of break characters)

it could just set up an array of pointers into the file (from ptrto ptr, with the appropriate key if alphbetical sort)sort this array in the specified way, and thenreconstruct the file.

8. NL parser for SETTHY

9. Analogies as an AI task

The kind of mul t ide imensiona l understanding of the meaningof words requried to do the miller analogies test is thenext level of understanding: like metaphors. Much ofhuman communication takes place at this level.

10. Aphasia

A computer program that understands speech can be brokenin certain ways in an attempt to make it exhibit variousaphasic behaviors.

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11. arithmetic learning

using John Brown's model of arithmetic bugs, and BarbaraSearle's voluminous Nicaragua data» try to get a handleon what bugs are more probable, what their semanticinterrac tions are, and what their relevance is to teachingarithmetic and to memory models. The Russian work onarithmetic aphasia may be incorporated here to!

12. semantic memory

accumulate evidence from the psychological literature aboutwhat the encoding strategy(s) for various types of memorymust look like. Wicken's stuff on release from ProactiveInhibition would be a good place to start.

13. AI Handbook

Work on the front end for the AI Handbook until it canreally discuss AI.

14. Parallel processing

It is evident that the human mind does a "blackboard" -likecontrol structure on many tasks* maybe all of them.Separate processes work in parallel and in cooperation.Rumelhart's model of reading is HEARSAY II re-worded (barley).There is much to be gained by going in the other direction,taking the psychological literature on neural subsrates,perception, attention and cognition and thinking about themin AI terms.

15. Learning 3/77

Learning is the key. everything from self-organizingsystems theory to Dreyfus' criticisms of frames. The ideais that there is some primitive representation (NIL ?) andsome primitive structure modifying processes. Tom Mitchell'sIJCAI paper looked interesting.

16. finite state diagrams : petri nets atns : ? 5/77

petri nets are an extension, in control structure at least,to FSDs. And ATNs are sort of like FSDs. What is thecorresponding extension? TW asked this question; my firstthought is that an ATN is really an extension of the FSD

formalism,

and petri nets are a more limited extension. sortof an abbreivation for a test against a global variable,similar to abbreviations in some production systems, wherecontrol is switched to another production without settinga (global) flag. It would be nice to get this controlstrucure stuff formalized.

**

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17. Vision 5/77

This was inspired by Harry Barrow's talk in C5224. Theheirarchical structure of a net-like feature detectionstrucutre can be turned around in a natural way, althoughthis doesn't appear to happen in the human VS. The ideais that a "feature detection" neuron, which looks and abunch of "lower level" outputs for a specific pattern, couldreally be a processor which when polled for some kind ofinfo from above, is able to poll the lower level for relevancharacterisitics. it's only reality is the output of thelower level processors, of course.

18. dutzen 6/3

Here is a key to the nature of language processing in man.In

German,

and Italian and French, there is a "familiar"declension of verbs. Actually though, the familiar languagehas a large non-intersecting body of idioms and specialadjectives and noun forms as well — it really is a separatelanguage. Yet it is perfectly natural to "dutzen" with oneperson and use the formal form with another in the sameconversation. The machinery to do this must be primitive;its just too easy. How d you think about language so thatsuch a switch is a primitive? And how did it come about thatno familiar form remains in English? When did it dissappear?

19. holograms 6/3

From Pribram's seminar today I realized that there is somemotivation for frequency encoding, beyond the 987. oblationstuff. In the recognition of

form,

for instance, the retinalimage can be significantly translated or rotated from itsusual position. no local machinery, no matter how manyneurons and perceptive fields are active, can account forglobal translation of patterns of this sort