Tos Lteklider @ ISI From: Newell @ CMUe1OA Date! {6 Apr 75 Re: AT ROAD MAP MEETING CCt#r Carlstrom @ ISI, Fields #@ ISI» Russel) @® ISI, Simon @ CMUnMIOA JMC @ SUMAT, LES @® SUWAI, CCG @ SUMAT, PHW @ MIT#AT, Feigenbaum ® [SI-e Amare] @ ISI, Nitssom ® SRI@AI, Sacerdoti @ SRI#AT Lick: Twentyefour hours haS Permitted some reflections on Monday's session ard the problems attendcent thereto, (1) To Pepeat what was generally accected, implicitiy and explicitivs by the AI seiemtists at the meeting! Tt is aoprepriater, givem the current general comtext and smecifice ARPA comtext, for the AI field to attempt a series of applications, Such apelications would be a good thing for AI generally, as well as for its specific relations with Ded, (2) Aliso to repeat? Apolication opportunities must he discovered, verified and exploited, Some fnstitutional means must be found to expedite this, For it is clear that the AI commumity by itself does mot have the expertise mor the commectioms ta find high pavoff activities, Dave Russell, at the end of the day, strongly posited a mechanism of a Randelike agency with the mission of fimdina apelication opportunities, verifying them, nutilding a bridge to the AI Labs- etc, There would also exists aS am adjumct to this, am AI Applications Tecmnical Group (or same such titled, consisting of rPepresemtatives of the various Labs, CMU, MIT, SRIWAI, SuUMAT, SU*HP, plus maybe others in related programs, such as FBN@SUS, SOC=SUS, Amarel, etc, This arove would be aprime forum and {nitiation point for these applications, I expressed some concern that such am agent could come tnto being in short eneyah orde* to satisfy the meeds of the day (implying that some temporary verkicle would have to be erected), but Russell seemed confident that such expedients were unnecessary, It would tke better his way, (3) It 48 extremely tmoortant to be sure that the payoffs of a specific apriication are real, It is too easy to get Sandbagged to have a seeming application turm to dross, Given that ARPA is prepared to spend large fractions of its Al eommunity fa relatively precious resource) om opreducing some soecific apollcationss it is critical to substantiate the need amd acceptability of am application, ARPA itself, though imside the Dod amd much closer to the apptication sites than tre Al commumity, does not itself have the expertise and, imoorptantive the time to examine the situations enough to make 3 Recognition and description (Perception) > Visien > Speech $ SFEIGENBAUMONEWELL 31 SUN 20"APRe75 o129PM > Langquaae Reoresentation Prohtem Solving Methods Centro! Structure Assimilation & Accomodation (Learning) vvvyv Withim each compoment ome can describe a series of structures Cor mechanisms) that are possibilities for this compoment, The discovery of each such structure and mechanism {8 an advance for AI amd a pesult, Verification, of course, if requireds it comes, usually, from i{nmcoroporation im several total systems, Knowledge about each mechanism grows with experimentation and theoretical Sharpeming, Suen knowledger again whem verified experimentally, constitutes scfentific results for Al, It cOomsists mostly of statements of adequacy or sufficiemey in specific task environments, Thus,e the statement "what are the results of AI" at a givem date is a listing of the various mechanisms (usually described by conventional technical mames), plus the associated statements of adequacy, This list grows over timer, amd it, rather than a Oarametrizatian of mow good are the systems that cam be produced constitutes the core tramsferable kmowledge of AI, This core ts indeed transferrahla,s, precisely because it comsists of the abstracted mecramiams which have beer Shown experimentally to be useful fm several task environments, T cannot praduce the lists of results for the total field, mostiy because they have mot teen extracted, labelled and organized im this way, T cam do it for ome subpart, that of problem solving methods, Heres, much that we know cam be given by specific methods (analaqous to the methods of mumerical amalysis), A fairly good list iss: > Generate and test > HETT climibing > Heuristic search > Search stragegys > Depth firsts, Breadth first, Best first, Progressive Deepening > Evaluation > Evatuatton functions, level of aspiration, Gupltcation avoidance, external limits Matcning Hypethestze and match Meams ends analysis Sunstitute & eliminate Range restrictian Abstraction planning vvvvveyv To find a short way to Say what we kmow, @gre about Hil] Climbingr takes more energy than I have at this wee hour, We do know the major things to beware of (Multi=modality, Mesas,s Ridges» Ciiffs', we do have some empirical things to say about whem Hd11 Climbing seems to work and when it doesn't, we do have a wav of classifving the refinements of the method (as Simplifed models of the hills which are used to predict the '