28048643
Generalization of an Elementary Perceiving and Memorizing Machine
The Elementary Perceiver and Memorizer (EPAM) is a context, and contains sufficient information to retrieve the model of human associative memory and the processes of image of the stimulus from the association memory when human verbal learning, written in the list-processing language necessary. IPL-V. A generalization of the EPAM model (called EPAM The complex image building processes also provide an assoIII), based on earlier experiments on EPAM 11, has been dative mechanism by means of which earlier learning can formulated and programmed. usefully be brought to bear on later learning. New stimuli arc In EPAM 111. stimulus configurations and their internal learned by associating together and modifying images of images are allowed indefinite complexity of sub-parts. Complex stimuli already learned. The model is an attempt to resolve internal images are built by associating together, in list structhe problem of how meaningful learning differs from nontures, tokens for retrieving information about the sub-parts meaningful learning, from the association memory. Empirical explorations of the model are concerned generally The concatenation of tokens by the image building process with variations in behaviour as a result of varying stimulus allows for the association of stimuli in many different contexts. and response meaningfulness. The token signifies the occurrence of a stimulus in a particular 3.ieM6HTapHan nporpaMMa jhh "H3yqaioinHx MauaeT coSbiTHe CTHMyjia b oco66hhom KOHTeKCTe, a npiiuihh" (3IIJIHM (EPAM)) npeacTaß^weTCH Moaejibio cyTCTByeT b aaHHOM naineKe aocTaToiHO HHdpopMauHH, accouHaTHßiioil nfiMfiTH h npoueccoß H3yiemm mob <rro6bi BooCpawenHe CTHMyjia moskho 6mjio Bbißec" y iciOBCKa; saiman Mo,Te.-ib narmcaHa b bh«6 si3uko, hs accouHaTHßHofl naMHTH b tom cnyqae, eoJiH sto A-1H pa3paf)OTKH CnHCKOB IPL 15. Ha OCHOBaHHH OKaHCeTCH Heo6xo«HMbiM. pe3y.ibTaTon Go.ice paiiHHx nonwTOK cihopMHyjinpoCjioiKHbie npoueccw, no3Bo^aiouiHe EPAM 111 CO3Ba.iacb ii cnporpaHMHpoßa,iacb ooo6meHHaH Moae^b naßaTb BoofipasKeHHH, Taicwe 3anaxaioT accoimaTHBEPAM jiß.inioumncHTaX Ha3bißaeMoft EPAM 111. Hiiit MexaHH3M, npn noMoiiw KOToporo npeAbiAymne B EPAM 111, mc.no noaiacTefl KOMiuieKCHbix <~thonuTH MoryT 3<h<beKTHßHbiM o6pa3oM BJiHHTb Ha cnemv.iob h BiiyTpeiiHHx hx n3oGpan<eHHH MomeT decayroiiuie. Hoßbie CTHMyjiu H3yiaioTCfl b pe3yjibTaTe npe.ie.-n.no yc.iovKHfiTbcu. OioiKHbie BiiyTpeHHHe accoiiHpoßaHHtf (tbm caMMM h Mo«H<bHKau.HH) HsyienH3oopn>KeHHn-iiMeiomHeMHoroypoßHeflHH(})opMauHH hux panbine BOo6pajKeHHft. MoaeJib npeacTaßJineT o no.tCTpyuTypectpohtch cnocoSoM accouHHpoßaHHH coCoft nonbrricy peuiHTb 3a«aqy o tom, KaKHM oopa3OM 3iia«iKon miMRKOB, npw noMomH kotopux cBe^eHHH pa3JinqaTb 3HaiaromHe CBeaenHH ot He3HaiaiomHx. o no.macTnx mo>kho BUBO«HTb H3 accouHaTHßHofl 3MnHpmiecKHe onwTbi npoßOflHmnecH Ha Mo^e^b, naMtiTii. Boo6me roßopn, hmciot c paaHUMH BH«aMH noße-3auen.ieHne iiaMeKOB npoueccoM co3«aHHH boosaeHHn, npoHCxofIHWHMH H3 pa3HOBHfIHOCTH 3HaienHfi pn>Kenmi no3Bo.ineT accou.HHpoßaTb cTHMy^u, npoHCTHMy^a h oTBeTa. cxoanmiio H3 Miioro pa3Hbix kohtckctob. HaMeK 03HaLa machine elementaire pour percevoir et mettre en mblir Information concernant les sous-parties, a partir de la moire (EPAM) est un modele de la memoire humaine d'assomemoire dissociation. ciation. et dcs process de la facon humaine d'apprendre L'enchainement dcs signes au moyen de l'image eUabor^e verbalement, 6crit en langage IPL-V. En partant dcs r«sulpermet d'associer dcs excitations dans un grand nombre de tnts de tests anteVieurs, on a dlabore' et programme" une contextes diffe>ents. Le signe montre qu'il existe une excitagcneralisation dv modele EPAM, appele EPAM 111. tion dans un contexte particulier et une information suffisante Sur EPAM 111, les configurations d'excitation et les images peut, grace ace signe, regene>er l'image de I'excitation a inteneures de ces configurations ont une complexitc indefinie partir de la memoire dissociation, quand cela est necessaire. de sous-parlies. Dcs images internes complexes sont dlaborees Les procedis complexes gui 61aborent l'image fournissent en groupant. en liste de structures, les signes destine* a retaaussi un m6canisme dissociation, permettant d'elaborer faci405 IX, 1] automata theory lement la toute premiere information a partir de la toute informations ayant un sens, de celles gui n'en ont pas. derniere. Les tests empiriques dv modele se rapportent gene>alement On decouvre de nouvelles excitations en groupant (et en aux variations de fonctionnement, celles-ci £tant le resultat modifiant) les images d'excitations deja connues. Cc modele d'excitations et de reponses variables. est un essai de r6soudre le probleme de la differentiation dcs
36371266
Some Further Notes on a Class of Skew Distribution Functions
This note takes issue with a recent criticism by Dr. B. Mandelbrot of a certain stochastic model to explain word-frequency data. Dr. Mandelbrot's principal empirical and mathematical objections to the model are shown to be unfounded. A central question is whether the basic parameter of the distributions is larger or smaller than unity. The empirical data show it is almost always very close to unity, sometimes slightly larger, sometimes smaller. Simple stochastic models can be constructed for either case, and give a special status, as a limiting case, to instances where the parameter is unity. More generally, the empirical data can be explained by two types of stochastic models as well as by models assuming efficient information coding. The three types of models are briefly characterized and compared.
6846387
Causality and Model Abstraction
Abstract Much of science and engineering is concerned with characterizing processes by equations describing the relations that hold among parameters of objects and govern their behavior over time. In formal descriptions of processes in terms of parameters and equations, the notion of causality is rarely made explicit. Formal treatments of the foundations of sciences have avoided discussions of causation and spoken only of functional relations among variables. Nevertheless, the notion of causality plays an important role in our understanding of phenomena. Even when we describe the behavior of a system formally in terms of acausal, mathematical relations, we often give an informal, intuitive explanation of why the system behaves the way it does in terms of cause-effect relations. In this paper, we will present an operational definition of causal ordering. The definition allows us to extract causal dependency relations among variables implicit in a model of a system, when a model is represented as a set of acausal, mathematical relations. Our approach is based on the theory of causal ordering first presented by Simon [22]. The paper shows how to use the theory and its extension in reasoning about physical systems. Further, the paper studies the relation of the theory to the problems of model aggregation.
29911339
Artificial Intelligence: An Empirical Science
My initial tasks in this paper are, first, to delimit the boundaries of artificial intelligence, then, to justify calling it a science: is AI science, or is it engineering, or some combination of these? After arguing that it is (at least) a science, I will consider how it is best pursued: in particular, the respective roles for experiment and theory in developing AI. I will rely more on history than on speculation, for our actual experience in advancing the field has much to tell us about how we can continue and accelerate that advance. Many of my examples will be drawn from work with which I have been associated, for I can speak with greater confidence about what motivated that work and its methods (and about its defects) than I can about the work of others. My goal, however, is not to give you a trip through history, but to make definite proposals for our future priorities, using history, where relevant, as evidence for my views.
62731399
NAS parallel benchmark results
The NAS (Numerical Aerodynamic Simulation) parallel benchmarks have been developed at NASA Ames Research Center to study the performance of parallel supercomputer. The eight benchmark problems are specified in a 'pencil and paper' fashion. The performance results of various systems using the NAS parallel benchmarks are presented. These results represent the best results that have been reported to the authors for the specific systems listed. They represent implementation efforts performed by personnel in both the NAS Applied Research Branch of NASA Ames Research Center and in other organizations.<<ETX>>
8839735
Internal representation and rule development in object-oriented design
This article proposes a cognitive framework describing the software development process in object-oriented design (OOD) as building internal representations and developing rules. Rule development (method construction) is performed in two problem spaces: a rule space and an instance space. Rules are generated, refined, and evaluated in the rule space by using three main cognitive operations: Infer, Derive, and Evoke. Cognitive activities in the instance space are called mental simulations and are used in conjunction with the Infer operation in the rule space. In an empirical study with college students, we induced different representations to the same problem by using problem isomorphs. Initially, subjects built a representation based on the problem description. As rule development proceeded, the initial internal representation and designed objects were refined, or changed if necessary, to correspond to knowledge gained during rule development. Differences in rule development processes among groups created final designs that are radically different in terms of their level of abstraction and potential reusability. The article concludes by discussing the implications of these results for object-oriented design.
8638136
The marketplace of high-performance computing
In this paper we analyze the major trends and changes in the High-Performance Computing (HPC) market place since the beginning of the journal ‘Parallel Computing’. The initial success of vector computers in the 1970s was driven by raw performance. The introduction of this type of computer systems started the area of ‘Supercomputing’. In the 1980s the availability of standard development environments and of application software packages became more important. Next to performance these factors determined the success of MP vector systems, especially at industrial customers. MPPs became successful in the early 1990s due to their better price/performance ratios, which was made possible by the attack of the ‘killer-micros’. In the lower and medium market segments the MPPs were replaced by microprocessor based symmetrical multiprocessor (SMP) systems in the middle of the 1990s. There success formed the basis for the use of new cluster concepts for very high-end systems. In the last few years only the companies which have entered the emerging markets for massive parallel database servers and financial applications attract enough business volume to be able to support the hardware development for the numerical high-end computing market as well. Success in the traditional floating point intensive engineering applications seems to be no longer suAcient for survival in the market. ” 1999 Elsevier Science B.V. All rights reserved.