JPRS ID: 9340 USSR REPORT CYBERNETICS, COMPUTERS AND AUTOMATION TECHNOLOGY

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JPRS L/9340
8 October 1980
IJSSR Re ort ~
p
CYBERNETICS, COMPUTERS AND
AUTOM~4TIUN TECHNOLOGY
- CFOUO 16~80)
FBIS FOREIGN BROADCAST INFORMATION SEF~VICE -
FOR OFr [f_'IAL USE ON~,Y
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JPRS L/9340
8 actober 1980
- USSR REPORT
CYBERNETICS, COMPUTERS ANO AUTOMATION TECHNOLOGY -
~ (FOUO ~.6/80)
CONTENTS
HARDWARE -
~'TO T_zotimpeks Advertises Disk Packs 1
Expansion of the Capabilities of a T~letype Version of
the Elektronika-SS Microcompuzer Switching Syatem 2
' SOFTWARE
Program Testing Using Symbolic E~:ecution 6
APPLICATIONS
Automated and Aistomatic Control Systems 19
Collective-lise Computer Centers 67 -
Designing Collective-Uae Computer Centers 72
Standardization of the Planning and Design Solutions for
Various Categories of Computer Centers 85
Analysis of the VTsKP SLbscribera and Their Problems 86
Problems of Determining the Cost Benefit of the V'TsKP 95
Creation of the Collective-Use Main Computer Center
of National Economic Plan;ning and Control of the
Latvian SSR 96
Multicomputer Complexes Based on Older Models of the YeS EVM
[Unified Computer Syetem) 104
Data Transmission in Co1lECtive-Use Computer Centers 109
! .
- a- [III - US5R - 21C S&T FOUO]
n/1T nT1'.'IT n+ -
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' Organization of Remote Subscriber Access to the Data
Procesaing Syatems at the VTsKP 111
- Library of Algorithms and Programs ~or the VTsKP _
[Collective-Use Computer Centers] 115
Means of Formalized Description of the VTaKP Data 132
Automation of the Preparation of Production Based on -
Machine Tools With Digital Program Control at the VTsKP 135
PtJBLZCATIONS
Artificial Intelligence 137
1
' - b - -
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Haxdware
VTO IZOTIMPEKS ADVr;RTISES DISK PACKS
R3ga AVTOMATIKA I VYCHISLITEI,~NAYA TEKHIJIKA in Russian No 1~, 1980 p 100
~Text] VTU IZOTIMPEKS DISK PACKS
Technical P~ameter Ye5 5053 YeS 526~. YeS 5269-type YeS 5266 YeS 5267
Capacity (Mbits) 7.25 29/58 2.?~5/5 lo0 200
Number of disks 6 11 1 12 12
Nvmber of recording ~ -
surfaces 10 20 2 20 20 ,
Track density (tpi) 100 100/20Q 100/200 200 1~00 -
Recording density
(bpi) 1100 2200 2200 1a1~00 1t1~00
Compatible with
IBM disk pack 1316 2316 541~0 3336 3300.11
Specification # 2864 3564 3562 4337 5653
Exporter: VTO IZOTIl~IPEKS
Sofiya, ul. Chapayeva, 51. Telephone: 73-61. Telex: 022731, 022732
COPYRIGHT: Izdatel'stvo "Zinatne", "Avtomatika i vychislitel'na~a tekhnika",
1980
- L412-PJ ,
CSO: 1863
1
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EXPANSION OF THE CAPABILITIES OF A TELE~YPE VERSION OF THE ELEKTRONIKA-S5 .
MICROCOMPUTER SWITCI~CNG SYSTEM
~ Riga AVTOMATIKA I VYCHISLITEL~NAYA TEKFINIKA in Russian No 3, ?980 PI? 5~-51
[Article by A. G. Novik, manuscript received 26 Jul 79] f
[Text] The Elektronika-S5 family of microcomputers has a multilevel -
interrupt system. The microcomputer is provided with the standard softwaxe
and ~iardwaxe necessary to register and service interrupts in real
time. For this purpose a higher level 4~bit interrupt register (RPVLJ) is
provided in which one bit is reserved to d.isplay program interrupts.
Although the RPW processes a generalized interrupt named by the
program interrupt, its initiation ma~r also be hardware-generated. At
the ftPW level, inte~rupts are processed by microprogram. The general-
ized signal of the program interrupt is shaped on an AND-gate between
the 8-bit program interrupt register (RPR) and its mask. Interrupts _
at the R,PR level are serviced by programs of the teletype version of the
switching system (TVDS). For TVDS, the job is the program unit. A software
or hardware initiated job program is equivalent to the appearance of the
signal "log 1" in~one of the RPR digits. Up to Fight jobs can be handled
simultaneously, two of which are assigned to TVDS. Jobs have absolute
priorities. When a lower priority job is interrupted by a higher priority
job, execution of the first one ceases and the problem having the higher _
priority is triggered. Because the general registers are doubly addressed
memory cells, the basic information on the interrupted program is
automatically retained. The systems table containing the origin of in-
- cluded jobs is generated with the I}ispatcher.
To develop very flexible, structured program software for a control system
for technological processes, it is inadequate to have the six jobs '
standaxdly implemented on TVDS. To expand TVDS, each RPR bit is linked by
_ hardware to ~he program interrupt preregister (PftPR) from the S/0 channel
field of the microcomputer (digital inputs and outputs TsW). S~ai.tching
by external computer coupling makes it possible to produce such a
connection and also process pulsed initiation signals coming from the
~ control object. Each bit of any of the six PR.PRs identifies tY~e =
independent sub-task initiated by the program or by the hardware (hardware
, connection of jobs is realized by extA~^r.;.l connections). At the pre-
register level, sub-tasks have a re 2ative priority in sequence. The
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c~~. ,~~..,eo.,~. ~ 1 ~ .
(2) a,sv (3)
_ct,.~ rte _ ( 4 )
`r~� ( 5 )
, n�~ ~ ~r mn, ~aw ~ars ~rsa
~6 ~8) ~g) ~1~) ~11)
I
A~~^~ 2
_ ; P,r(13)
nmt (1 ~om~a t 1 ~ ~
~p~e (16 )
- naunqo
~ 18 So~a 370 �
Figure 1. Structural diagram of two-rank interrupt system and its control
system. [Key: 1. generalized interrupt; 2. reserve; 3. RPW, WV; q,
_ console; 5. RPR; 6. PRPRl; 7. PRPR2; 8. PRPR3; 9. PRPR4; 10. PRPR~; 11.
PRPR8; 12. controller; 13. PRP; 14. PRPRi; 15. job i; 16. interruption; 17.
monitor; 18. job ZPRi~].
system can contain up to 48 jobs in all (with a TsW register 8 bits in
length). Sub-tasks of a single group do not interrupt one another, but are -
executed in sequence, because to retain current information in interrupted
programs it would take about 1.5 kbytes of main memory_ Interruptions at
the level of the preregister are serviced by a re-enterable "r[onitor"
subroutine which accarding to the number of the initiating control job,
information in the preregister and data in the systems table of preregister
jobs transmits control to the corr.esopnding sub-task (Figure 1). This
_ approach was used i~n [1] .
The process of elaborating program software boils down to specification of -
program modules which realize independent functions and their connection
into the system. In some cases aith logically complex and branched
technological and computer algorithms it may turn out *h.at 48 jobs are not
enough. In that situation, program and hardware supp~rt of interruption
processing may be supplemented by yet another level ~of processing. Each
PRPR bit is ~onnected to the lower level interrupt preregister (PPNU) from -
among the remaining free digital input/outputs. For a different ap-
plication it may not be mandatory to connect all digits of all PRPR to the
corresponding low level registers. The maximum number of sub-tasks for the
Elektronika .S5-G1 micre;,omputer with all possible connections to the PPNU
3
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- --~~r+ \JVU Vl\L�
~u~Fyr.ear ~rpddow,e ~ 1,
~2~ ~x ~''~ae~3 (4)
~ (6) ~ ~(5)
~xrm, r ~ ~
- O /17NM ~
I/iMIR /YM/JJ' - - - - � /17hNJ/ N7/Ndit
l~l
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~i
/'~7rrumr~o
/YNNir l,
~ 1 abva !/1P i~
_ ` C~~,~ ;
(14 ~ loci~w S~Io 1.
Figure 2. Structural diagram of three-rank interrupt system and its
controi system. [Key: l. generalized interrupt; 2. reserve; 3. RPW, UW;
4. console; 5. PRP; 6. PRPR1....; 7. PpNU 10.....; 8. controller; 9. RpR,
- PRPRi, PPNUik; 10. job i; 11, monitor; 12. job ZPR ij; 13. supervisor; 14. '
job ZPR ijk).
_ !
is 384, while for the Elektronika SS-02 modification it is 1,536. These ~
computers have different TsW capacity: S5-Ol has 8 bits, SS-02 has 16 ~
bits.
For distributed computing systems based on mi.croprocessors and multi- '
processor complexes with many initiating signals, the three-rank structure ~
of the interrupt system is most acceptable (Fig. 2). The systems table for i
low level jobs may be only partially filled, because the presence of all
sub-tasks is not mandatory and is defined by the user in the development of
program modules. If some jobs are not fitted in the system, the initial ~
address of the program trap which ~vill return control to the Supervisor
should be put in the a~propriate line of the job tab?e to increase i
re'.iability of program support.
_
- From the standpoint of a standard TVDS Controller, the two systems
described do not generate new problems at the control level, but specific
subroutines from one of six high level problems. This difference is hidden ~
from the user in the internal organization and structure of programs of ;
interrupt processing. He is given the opportunity to operate with jcbs
having preset priorities and established service discipline. ~
The principle of modular programing for the Elektronika-S5 family of ~
~ ~
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microcomputers is realized by expansion of TVDS with re-enterable programs
which may be masked in peripheral memory and be entered into the interrupt
processing system by assigning the proper addressses in the control job
origin table.
= References
1. Izakson-Demidov, Yu. A., Kallistratov, V. A., Novik, A.G.: Design of
an automated control system for large electrothermal installations
based on the use of microcomputers. Mat. seminara "Kiberneticheskiye
- problemy ASU tekhnologicheskimi protsessami", Moscow, 1978 pp 58-64.
COPYRIGHT: Izdatel'stvo "Zinatne", "Avtomatika i vychislitel'naya tekh-
nika", 1980
[ 399-8617' ]
8617 -
CSO: 1863
~
5
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Software
UDC 681.3.06
PROGRAM TESTING USING SYMBOLIC EXECUTIO+~
Moscow PROGRAMMMIROVANIYE in Russian No 1, 1980 pp 51-59
~Article by Yu. V. Borzov]
[Text] Program testing using symbclic execution is
discussed. Efficient automatic systems for testing
programs are surveyed. Problems in developing them
are d~scussed.
From t?^e iiistory of Automation of Testing
The first s teps in automating testing were attempts on machine generation
of tests us ing descriptions of the structure of the input data. This was
_ done as ear ly as in 1962 by R. L. Sauder [1], and then developed later by
H. T. Hicks [2], K. V. Hanford [3] and others. However, in these cases
the tests only externally resembled (in their f~rmat) the data that the
program was compiled to process. Specific test values were usually gene-
rated randomly, and therefore, there was no guarantee that the generated
tests would pass through all the branches of the program. Practice showed
that this approach fo;,nd effective application in debugging translators.
This is also indicated by the use of this approach in the USSR in RTK [ex-
~ pansion unknown] technology [4]. This approach was not used extensively
in programming other types of problems.
From another aspect, development focused on systems automating individual
functions of testing. Systems were developed to gather statistics on the
program tes ted (number of executed statements, maximum and rniniteum values
- of variables, execution of defined � relations between variables, etc.)
[5-12]. Some systems could automatically determine the conditions that
the input d ata had to satisfy for the program to execute a given path
- [5, 13]. Systems [14, 15] produced a set of paths of the program, that
_ contained all branches of the program in the aggregate. ~
;
~
All this information could be usefully exploited when compiling tests, i
which, however, had to be conducted manually.
'
~
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'r'OR nFcICl'~1L [iSF. ONL~'
Finally, in the mid seventies, several groups independently and practical-
ly at the same time developed experimental testing systems using symbolic
execution of programs. Some of them had the primary goal of full automa-
tion of generating tests [13, 16-24] (in the process, the tests had to pur-
posefully and adequately check out the program, in contrast to randomly
generated tests). Others tried to look at the operation of a program -,ot
in a specific test~ but in a whole clas s of them. The ultimate goal of
this execution is to define the corres p ondence between classes of input
and output data [18, 25-27].
Every programming language has its execution semantics describing:
a) the range of definition of program variables (usually these are numbers);
b) permissible operations on values of the variables (addition, subtraction,
etc. of numbers); c) and rules for transferring contrel during execution
of the program.
It is also expedient to define the so- c alled semantics of symbolic execu-
tion, under which formulas on symbolic values are produced ~s the results
(one can say that ~ve are thus replacing arithmetic with algebraic calcula-
' tions).
Symbolic Execution, Type I. Let us cal 1 a path the finite sequence of pru-
' gram statements a~, a2i a~, ak+l, if for every i(1 5 L/~ k) ,
- transfer of control from-Q;~ to a+l, is formally possible.
The goal of the first type of symbolic program execution is to determine
for a given progr~m path what conditions the input data has to satisfy su
that control is transferred precisely along this path when the program is
executed with this data.
Example. Let us assume the program ~
POWER: YROCEDL'RE(X,Y); i
Z = i; 2
J = i; 3
LAB: IF Y~= J THEN 4 -
DO;Z=Z. X; 5
J ==7+1; 6
GU TO LAB; FND; 7
RETURN(Z); 8
END POWER; 9
Let us examine what conditions the inpu t data must meet for the path 1, 2,
3, 4, 5, 6, 7, 4, 8 to be executed. Th is can be tracked if after execu-
tion of each statement we record the va lues of the program variables and
the relationships between them (let us call this the status). Then the
initial status will appear to us like this:
- a) values: X^ al~--y ~ a~~ ~ and J are not initialized;
- b) relationships: none.
7
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Here ai and a~ are symbolic designations of the input data. Given below
are the statuses after execution of statements 2, 3, 4, 5, 6, 7, 4, 8:
2- a) X= a1, Y= a=, Z=!, J is not intialized, b) none -
3- a) X= al, Y= a~~ Z= i, J= i b) none
4-a) X=al, Y=aY, Z=1, J=1 b) aa]1
5-a) X=al, Y=a=, Z=1~ac.~, J=1 b) a~]1
~G-a) X=al, Y=a~, Z=1+al, J=i-}-i b) az~i .
7-a) X=al, Y=a=, Z=1�a1,J=1-}-i b) as~l
~ 4-a)X=a1iY=aa,Z=1.a1,J=i-}-i b)az~i,aZ
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FOR OFFICIAL USE ONLY
APPROVED FOR RELEASE: 2007/02/08: CIA-RDP82-00850R000300040020-2

APPROVED FOR RELEASE: 2007/02148: CIA-RDP82-44850R000300044420-2
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APPROVED FOR RELEASE: 2007/02/08: CIA-RDP82-00850R000300040020-2

APPROVED FOR RELEASE: 2007/02148: CIA-RDP82-44850R000300044420-2
- - FOR OFFICIAL USE ONLY
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COPYRIGHT: Izdatel'stvo "Statistika", 1979
[389-10845]
10845 131 -
CSO: 1863 FOR OFFICIAL USE ONLY
APPROVED FOR RELEASE: 2007/02/08: CIA-RDP82-00850R000300040020-2

APPROVED FOR RELEASE: 2007/02148: CIA-RDP82-44850R000300044420-2
FOR OFFICIAL USE ONLY
MEANS OF FO~MALIZED DESCRIPTION OF THE VTsKP~ DATA
Moscow VYCHISLITEL'NYYE TSENTRY KOLLEKTIVNOGO POL'ZOVANIYA in Russian 1979
signed to press 4 Apr 79 pp 227-240
[Article by G. M. Belash, 0. M. Veynerov, M. S. Kazarov]
[Excerpts] Let us proceed with the analysis of the SFOD [means of
formalized description of data]. Let us briefly consider the SFOD
oriented toward the data administrator and the applied programmer, dis- .
cussing only the events illustrating the arguments and conclusions pre-
sented below.
Let us remember that the data administrator develops the structures of the
data in the base, constructs the data base system, achieving efficient _
functioning of the base for the entire complex of problem~ ~programs using
it. The administrator is responsible for the physical organization of the
data.
The layout of the data b ase reflects the general structural laws character-
istic of the entire set of snecific data fixed in the base. These laws
- basically are determined by the logic of the sub~ect region depicted in
the data base. The layout of the data base is described by the language
means of definition of the data (YaOD) which must provide for tt~e follow-
ing:
The description of various types of structures generating the structure
of the data base (that is, the description of the parameters of these
structures names, formats, types of data, indication of keys, and so
on);
Description of the relations between the generating structures and the
layout of the data base.
In addition, the YaI~ID [data manipulation language] can be used to
describe the layout of the protection of the data base from unsanctioned
access and the attributes of the protection layout. _
*Collective-use computer center
_ 132
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FOR OFFICIAL USE ONLY
The applied programmer (the problem-oriented user) communicates with the
data base using the data manipulation language (Ya1~ID), the operators of
which either are included directly in the procedural (the procedure-
ortented) language or they are formed as refer ences to the procedure.
The functions of the YaOD [data definition language] are as follows:
The specification of the reference to the data base (search, reading, _
replacement, removal, and so on); -
Definition (by indicating the types of structures and the corresponding
names) of the attributes of the base layout us ed to localize the data
with which the manipulations are performed;
Indication of the specific values of these att ributes (and also tne .
logical functions of the values, the ranges of values, and so on).
The Ya1~ID can be used also to indicate the values of the protection keys.
The system s committee KODASIL has performed a~etailed analysis of the
structural principles of the YaOD and the YarID, the results of which are
_ reflected in [1]. On the basis of the performed analysis and generaliza-
tion of th~e KODASIL, proposals have also been developed with respect to
standardizing the YaOD and Ya1~ID. These proposals have been described in
detail, for example, in [2].
When creating the first stages of the VTsKP, obviously the "Oka" SUBD
- became widespread, which at the present time is the most powerful among
the systems of this type available for applica tion.
The structural princlples of the means of definition and manipulation of
data "Oka" have differences from the principles of the KODASIL. Accord- _
ingly, let us remember the basic principles of the organization of the
YaIrID of the "Oka" system (YaNID is not a system term) . This is needed *_o
compare the YaNID and other means of formalizing the de~cription of data
which will be considered below. -
"Oka" supports the following types of structures: field, segment, entry,
data base.
~ The entry is the set of segments among which the hierarchical relations
are established. The da~a base is an ordered set of entries.
The system provides for the following types of references tu the data
base.
GET UNIQUE (GU).
GET NEXT (GN). _
133
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L~va\ vC[tVLC1L 6i.~ti vi~a~?
GET NEXT WITHIN PARENT (GNTP) . ~
GET HOLD UNIQUE, GET HOLD NEXT, GET HOLD NEXT WITH PARENT.
REPLACE.
INSERT.
The analysis of the means of formalized description of data performed in
the article has made it possible to determine the relation of the SFOD
oriented for people of different categories, interacting with the auto-
mated data bank of the VTsKP and to draw the following basic conclusions .
1. The data definition language with respect to the SFOD of the applied
programmer performs the functions of a metalanguage of the description of
the grammar.
2. The organization of modern SFOD of tYle applied programmer SUBD with
the base language insures potential possibility of constructing the
simp le translation algorithms from the IPYa [information retrieval
language ] o f the f inal us er . I
- 3. The requirements which must be satisfied by the IPYa of the final ~
user of the automated data base agree well with the principles of the
organization of the modern SFOD of the automated control system. ,
- 4. The orientation to the mentioned requirements permits the construction
of a simple inte~face of the SUBD with the file user adj usted to the
- specific application.
BIBLIOGRAPIiY
1. INFORt~IATSIONNYYE SISTEMY OBSHCHEGO NAZNACHENIYA [General-Purpose
Information Systems], translated from the English, edi~ed by
Ye. L. Yushchenko, Moscow, Statistika, 1975.
2. Stolyarov, G. K. Survey of the Proposals of the Working Group of
the KODASIL with Respect to the Data Bases," ALGORITMY I
ORGANIZATSIYA RESHENIYA EKONOMICHESKIKH ZADACH [A1goNot~mSMoscow
- Organiz3tion of the Solution of Economic Problems], , ~
Statistika, 1974.
COPYRIGHT; Izdatel'stvo "Statistika", 1979
[389-10845]
10845
CSO : 1863
~.34
~ FOR OFFICIAL ~TSE JNLY
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APPROVED FOR RELEASE: 2007102/08: CIA-RDP82-00850R000300040020-2
FOR OFFICIAL USE ONLY
AUTOMATION OF Ti~E PREPARATION OF PRODUCTION BASED ON MACHINE TUOLS WITH
DIGITAL PROGRAM CONTROL AT THE VTsKP
Moscow VYCHISLITEL'NYYE TSENTRY KOLLEKTI.VNOGO POL'ZOVANIYA in Russian 1979
signed to press 4 Apr 79 pp 256-267
[Article by P. L. Stepanov, V. Z. Yampol'skiy]
[Excerpt] Summing up the results of what has b een stated, let us consider
two problems of the operating efficiency of the "Stop-2" system.
1. The analysis of the operating exper ience of the machine tools with
digital program control indicates that for effective utilization of them -
in small-series production it is necessary to rep air about 200 UP [control
programs] per year per machine tool. At the same time for the preparation
of one control program of wedium complexity (on the order of 150 frames)
using the existing SAP [automated preparation systems] the following are
required:
About 10 to 15 hours to design the technological process;
About 40 hours for the preparation and checkout (including on the machine
tool) of the contral program.
Here the required machine time will be 0.5 to 0.6 hours, and the calendar
preparation time and checkout time for one control program will reach
20 days, ~
The analysis of the possibilities of the "Stop-2" system indicates that as
a result of using the standard elements of control programs the labor con-
sumption of the technological process design is lowered by 2 or 3 times,
and the time for checkout of the control program is reduced by 30 to 40%,
for the necessity of debugging uork is excluded in the sections of the
cantrolling programs where standard solutions were used. The calendar
time for the preparation of one controlling program decreases as a result
of the dialog mode of operation of the remote user from 20 to 4-5 days,
that is, it is redt~ced by 4-5 times by comparison with the existing systems. -
2. The operation of systems similar to the "Stop-2" system is a fruitful
and economically expedient basis for the interrelation among the industrial
enterprises and the VTsKP. On the one hand, the enterprises avoid the
necessity for bearing the expenditures on the acquisition and the operation
135
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_ _
of the expensive all-purpose computers, the operation and the development
of the labor-consuming and complex software for the automated systems to
prepare the control programs, including the developed libraries of standard
elements of the control programs. On the other hand, the collective-use
computer centers obtain a reliable partner with a stable demand for
machine time in the person of the industrial enterprises.
Actually, at the present time the flaet of machine tools with digital
program control in the country is on the order of 30,000 which determines
the demand for the preparation of 6 million controlling programs per year
and, consequently, the requirement along the order of 2-3 million hours
of: machine time.
With respect to the VTsKP of the industrial region it is possible to make
the following calculation. Servicing 10 or 15 large industrial enterprises
with a f leet of machine tools with digital program control on the order of
50 units in each, the VTsKP can realize 50,000 to 75,000 hours of commercial
~ machine time per year for operations with respect to automation of the -
technical preparation of production on machine tools with digital program
control alone. Considering that the system permits simultaneous servicing -
- of up to 10 subscribers, the demand for the calendar machine time wi11 be
about 6,000 hours per year, that is, a daily load of one of the YeS-1033 ~
computerG will be reached. The planned increase in the fleet of equipment
with d~gital program control by 20 to 25% by 1980 insures further stable
growth of the machine time demand. Here it must be noted that when pre-
paring the control programs the operating time of the processor will be 50
to 60%. This is a great prerequisite for increasing the profitableness of -
the collective-use computer centers as a result of solving the background
problems and to reduce the expenditures of the enterprises to lease machine
time as a result of lowering the price of one ho~,~r of machine time in the
time-sharing mode of the computer.
COPYRIGHT: Izdatel'stvo "Statistika", 1979
[389-10845]
10845 ~
CSO: 1863
136
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Publications
ARTIFICIAL INTELLIGF~ICE
_ Kiev ALGORITMY RAZUMA in Russian pp 10-13, 221-223
~Book by Nikolay Mikhaylovich Amosovi "Intelligence Algorithms." Printed
by the order of the D33torial Boaxd on Popular Scientific Literature of
the Ukrainian SSR Acaaeiqy of S~iences.
Number of copies printe~ 37,000.
~Additional selections from this source appeaxed in a previous issue of this
: report see JPRS L~9204, 23 Jnly 1980, pages 148-149]
_ ~Text~ Introduction
Intelligence mechanisms interest scientists of various specialties. For
psychologists and physiologis+,s it is the theory of their sciences; for
cyberneticists it is ways to create artificial intelligence (SI). In
this book be~.ng of~er.od to -the att~n~ion of the read~r: I x~,l~ attempt to
present concepts on this problem as a result. of the develop;uant of
work be:.ng done in the Biocybernetics Department of the Cybernetics In-
_ stitute of the Ukrainian SSR Academy of Saiences and which started in 1962.
New ideas differ considerably from those publishec~ previously [1-3].
= The work "~,lgorithm" was not introduced into the title of the book by chancei
. I believe tha,t it is possible to "pigeonahole" the most complex ma.nifesta-
tions of intelligence even with prospects of its development t o a higher
level than huma.n intelligence. Evidently I will not be able to convince
sceptics for this it is necessaxy to reprod.uce intelligence algorithms
in programs. Regrettably, there axe great diff.~.culties in this path. Per-
haps the cited ideas will h~elp the enthusiasts of this problem? 1 warn
tha.t the subject is exceptionally complex to understand inasmuch as it
lies at the junction of physiology, psychology, engineering and even phi-
losophy. For simplification I will use block diagrams widely.
Intelligence is defined as a totality of ineans and methods to control com-
plex systems by operating their models,directed by criteria of opt imal
control. Modern science and technology ma.ke it possible to reproduce
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models and operate them by technological means and thus sepaxate intelli-
, gence from the brain to which it is usually related. The difference be-
tween the cited definition and the multitude of others consists in that
it stresses this last circumstance.
Thus, speaking of intelligence, we will have in mind this totality of
means and methods of control independently of whether it is realized in
biological systems or by means of artificially created technological means.
This use of the term "intelligence" is not generally accepted. However, it
is closely related to the basic idea of this book. In those individual
cases where we speak only about natural human intelligence, this will be �
mentioned specifically. As far as the term "artificial intelligence" (12)
is concerned., it, as will be assumed, will be used to designate vaxious
kinds of technical implementations of intelligence models.
We will dwell on the basic concepts in order to enter the scope of the
problems discussed ~adually. First of all~ it is necessaxy to sepaxate
out two opposite approaches to simulating intelligence. Conditionally~
they may be called network and algorithmic. Correspondingly, we will dif-
ferentiate between two types of models network (SI) and algorithmic (AI)
intelligences.
. ~t) , (6}
06 ~2~ i sn - nn
- --{P]~-I ~ _ ~ l -
~ `J~ ~0
.
3 "
_ -M~ I
Fig. 1. Block diagram of network intelligence (SI).
i. ob control object 6. PP permanent memory
2. Rts receptora 7. RM recognized model
3. E-- effectors 8. Kr cri~eria -
4. VP temporary memory y. P'ID action model
5. PM primaxy model 10. "8rain"
The simplest block diagram of a network intelligence repsesents aome complex
three-dimensional structure. It is sensed by sensors receptors Rts ~for
exa,mple, eyes) and is transmitted by signals to the "brain," where it is
converted to the primary model PM. To a first approxima,tion, PM is repre-
sented by a flat two-dimensional structure, ma.de~ up of excited, activated
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components of some network conditional "receptor field." The object
is recognized by superimposing PM on a multitude of figu res of inactive
components, joined by "itineration" ties. These inactive model figures
- represent models of already Imown objects and make up permanent memory PP.
It~. accordance with the principle of operation, SI components that ma,ke up the
permanent and temporary ~or active ) memory are one and the same; they _
differ only in the level of activity. The superimposition of the prima,ry
model on the network selects and activates one recognized mod.el of figure
RM and from it a controlling effect is connected to the ob~ect. This
effect is represented by the action of model Nm~ controlling effectors E.
The recognized model of the object is tied to s~everal action models; the
selection of the neQded one is determined by criterion Kr. The action
madel activated from RM and Kr transmits the activity to the effectors in
which the control signals are converted into the mechanical energy of the
controlling effect. Thus,in a network intelligence "the actions with the
models" axe represented by the change in the activity of the components of
the networks in which the models axe built in.
(i~ ~2~ ~3)t4) ~5) -
D6 Pu na; n, n.~
PM
` ( / Ka Ql1
3 q~ uQ
t1i~ Cto) 9
Fig. 2. Blocx diagram of an algorithmic intelligence (AI)i
1. ob control object 7. Kr criteria
2. Rts receptors 8. DP long-term memory
3. PMi primary structural model 9. Nm digital action
model
- 4. P1 converter 10. P2 converter
5. PMZ primary digital model 11. E-- effectors
6. RM recognized model
Fig. 2. shows a block diagram of an algorithmic intelligence. It staxts
- functioning in the same way as in the SI, as a result of receptor Rts
operation priruary structural model PM~ forms a two-dimensional structure
in the same plane~. However, it is immed.iately read-out by convarter P1,
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transforming it into a lineax, single-dimensional model consisting of a
- set of digits PM . All further actions axe impl~mented by thia model.
PM recognition cons~sts of its sequential comparison with standard models
recorded by the same code in long-term memory DP, where recognized model _
PM is located. Action model NID is selected in accord.ance with this model. ~
This process is implemented by sorting models in the long-term memory under
the control of criterion Kr. The action nodel is transferred to converter
P2 where the digital code is converted into effector E controlling signals
a,r~d through action controls to object Ob. ;
The basic difference between SI and AI is in the structure of the memory ,
and the resulting vaxious actions with models. However, in both types of ~
intelligences~ the }~'rinciple of controlling the models from the side of ;
the control criteria through their activation is preserved.
The creation of SI ~nd AI assumes the use of various methodological ap-
proaches to simulating one and the same object human intelligence.
Each of the approaches has its strong and weak points. Therefore~ in
designing actual II s~stems, it may be feasible to represent several in-
telligence functions in the form of network models and some others in
the form of algorithmic models. The general principle here is as followsi
the lower the level of trie function being simulated in the general hierarchy
of intelligence functions, the more probable it is tha.t the network method
is the most efficient in reproducing the II.
The situation is that the implementation of many functions of the lower
level (such as, for instance~finding the recognized model in accordance
with the primaxy model-- see Fig. 2) means a great amount of sorting in
the long-term memory. Such sorting is most efficiently implemented in the
At
network models that realize paxallel processes of data reprocessing.
the same time, network mod.els have other shortcomings that limit the axeas
of their application. I will dwell ma,ny times on the compaxative analysis
of the advantages and disadvantages of network and algorithmic models.
A number of papers axe dedicated to problems of creating SI and investi-
gating their properties. Brief reviews of concrete results that have been
obtained axe given in the first section ofrinciplesk.nothmaking anyifurther
dedicate to the description of AI design p
special stipulations that, in its practical implementation, a number of
functions may be represented by network structures.
BIBLII~RAPHY
i. Amosov, N. M. "Modelirovaniye myshleniya i psikhiki" (Thinking and
Psychics). Kiev, 1965~
2. Amosov, N. M. "Modelirovaniye protsessov myshleniya" (Simulating _
Thinking Processes). KIBERNETIKA, 1968, No 2.
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3. Amosov, N. M. "Iskusstvennyy razum" (Axtificial Intelligence). Kiev,
1969.
4. Amosov, N. M. ; Ka,satkin, A. M. ; Ka,satkina, L. M. ; Talayev, S. A.
"Avtomaty i razumnoye povedeniye; Opyt modelirovaniya" (Robots
and Intelligent Beha.viori Simulation Experisnce). Kiev, 1973�
5o V~nogra.d, T. "Programma, ponimayushchaya ;~esltestvennyy yazyk" (A pro-
gram tha,t Understands Natural La,ngua,ge Moscow,.i976.
6. Kasatkina, L. M.; Ka,satkin~ A. "Heuristic Behavior Model,." In bookt
"Nekotoryye problemy biokibernetiki" (Certain Biocybernetic Problems).
Kiev, 1966, second edition, pp 21-36.
7� McCullough. Reliability of Biological 5ystems. In bookt Self-Ch~gan-
izing Systems. Moscow, 19~, pp 358-378�
8. Minskiy, M.; Peypert, S. "Perceptrons." Moscow~ 1971.
9. Sutro, L.; Kullmer, U. "Totality of Decision Devices for Robot Control."
~n book "Integro xobAty" (Integrating Robots). Moscow, 1973~ PP 112-163. -
10. Nielson, N. Artificial Intelligence, Moscow, i973�
il. Sa,muel~ A. "Certain ?nvestigations of the Possibilities of Teaching
' Machines on the bcample of Playing Dominoes." In Booki "Computer
Ma.chines and Thinking." Moscow, 1967, pp 71-112.
12. Newell, A.; Simon, G. "GPS Program Simulating the Process of Human
Thinking." It~id., pp 283-3~1.
13. Shenk, R.; Abelson~ R. "Scenario, Plans and Knowledge." In book~
~
Tr.IV. Mezhdunax. obyed,. kong. po II. "(Transactions of the Fourth
International Joint Conference on Artificial Intelligence). Moscow, _
1975s PP 208-220.
14. Faix, R.; Nielson, N. STRIPS System." In Book "Integr. roboty "
_ (Integrating Robots). Moscow, 1973~ PP 382-~3� -
15. Hewitt, C. "FIJINNER I~nguage for Provir:g Thearems in Robots." .In Int.
joint conf. Ar~. Intel. Wash., 1969, pp 295-301.
16. Hewitt~ C. "Procedural Imbedding of Knowledge in PLANNER." In Second
Ing. Joint Conf. Art. Int. Leningrad, 1971, Pp i67-182.
17. Robinson, J. A. "The Generalized Resolution Principle." N.-Y., 1968,
3~ PP 77-94.
= 1la.l.
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Table of Contents
Page -
Foreword 5
.~r?troduction 10
Artificial intelligence today 14
Basic stages and directions of investigations i~ ;
Certain results of simulating network intelligence 18 ~
Systems and models 31
Complex systems 33
Models 35
Perception and memory 38
Perception and control 38 ,
Generaliza.tion of perception . ~ '
Types and mechanisms of inemory ~'3 ~
Hypothesis on ~3nsmory mechanisms ~'6
Recall, generalization, forgetting ~
Ck~ im~+l~mentation of the hypothesi~ 53 _
Model actions 55 `
Activation of models 55
Compaxing models and recognizing images 607 i
Finishing the "phrase" recall 60
- Generalizati~n of models ~
Interaction between models 1n intelligence 63
Criteria, requirements, feelings~ ~timuli 66
Cla.ssificativn of feeling-requirements 73
Cycles of activity and tension 78
Time 80 -
82
Reality
The funetional act 85
Algorithms of a simplified funetional act 87 -
"Areas" of perception 95
Forecasting and determining the dynamica 97
Hierarchy of functional acts 99
- F~inctional act netk~ork i03
"Thoughts" 106
11~2
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Table of Contents (Continued)
Page
Consciousness and Subconsciousness 109 -
SUT-system of intensifying-retaxding 109
SUT in algorithmic intelligence 111
~tT'r and consciousness 114 _
' ~nctions of subeonsciousness 116
"Coordinates" of consciousness 120
Human intelligence level 122
Speech "~~3
Highest levels of consciousness 3~9 -
- F~cperience and imagination i53
Work and creativity ~ 157
Dreams and psychological illnesses 1~
ArtificiaT intelligence in huma.n society 170 ~
Ba,sic "individual" qua,lities of II 170
Society of a,rtificial intelligences 188 -
Ch the way to intelligence higher than the human one 195
Human intelligence and complex systems 195 ~
Method of heuristic simulation 199
~ Heuristic models of some complex systems 2~2
Axtificial intelligence higher than the human one 206
Design of algorithmic model of intelligence 209
Conclusion 216
Bibliography 2~1
CCIPYRIGHT~ IZDATEL�STVO "NAUKOVA DUMKA"~ 1979
[411-2291]
2291
CSO: 1863 ~D
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