=Paper=
{{Paper
|id=Vol-2843/shortpaper006
|storemode=property
|title=Construction of Intellectual Informative Systems (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2843/shortpaper006.pdf
|volume=Vol-2843
|authors=Mikhail Mikheev,Yuliya Gusynina,Tatyana Shornikova
}}
==Construction of Intellectual Informative Systems (short paper)==
https://ceur-ws.org/Vol-2843/shortpaper006.pdf
Construction of Intellectual Informative Systems*
Mikhail Mikheev, Yuliya Gusynina and Tatyana Shornikova
Penza State Technological University, 1a/11, Baidukova pas./Gagarina str., Penza, 440039,
Russian Federation
shornikovat@mail.ru
Abstract. A short summary of literature on the description of fundamental
methods and techniques, the implementation of schemes and computer pro-
grams for the construction of software for the implementation of self-
developing intelligent multimedia models is given. They are based on the con-
cept of multiagency, development in systems of artificial origin. In addition,
neural fuzzy software agent systems are used. Consider increasing the intellec-
tual value of advanced multimedia models using methods based on agent-
ordered methods. For an agent the functional model of the mixed type, in that
clear and unclear rules are used in playback modification of behavior, is built.
To analyze complex factors and conditions, functional dependencies over rela-
tions are used, as well as symbolic formulas for fuzzy logic over fuzzy symbols
and forms. With the help of transformations over attribute values, diagrams of
the rules for outputting the alphabet of the calculus are described. Their essence
in functional transformations. The abilities of theoretical-categorical representa-
tion of models of similar intellectual agents and further formalization of the
evolutionary formation of agent groups are discussed. In the future, it is as-
sumed to study the potential of a theoretical and formal representation of the na-
ture of neural fuzzy elements with various modifications of their components. It
is also intended to build mappings that can convert elements of one format to
elements of another format. The goal of all research is to develop new trans-
formations.
Keywords: Multimedia Systems, Intellectual Agents, Fuzzy Logic, Conversion
Agent, Neural Networks.
1 Introduction
Currently, multimedia systems are complex local multi-level batch associations [1].
The large streams of information pass through them. These systems are the link be-
tween the input and output effects [2], [3].
Multimedia multitasking. Intelligent systems, as one of the subspecies of multime-
dia systems, can also manage information [4]. Often managed from one location, or
by one decision maker. In addition, the task of summarizing systems helps solve prob-
*
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribu-
tion 4.0 International (CC BY 4.0).
lems of the decision maker in managing multimedia processes, and also allows you to
build structures that model the functioning of multimedia systems [5-6].
The study of these issues is devoted to the work of a number of domestic and for-
eign authors [2-7]. Their works describe the problems of analysis, synthesis, modeling
and construction of information telecommunication systems. Also named a number of
reasons for solving these problems:
─ systems consist of a large number of interconnected subsystems and elements in-
terconnected and subordinate to a single development goal;
─ the functioning of any element and subsystem within one system is not separate
from the others and depends on the position of each of them in the system;
─ some individual elements and subsystems within one system may develop incon-
sistently with each other, so their behavior may be described by complex func-
tional dependencies;
─ the behavior of some subsystems and elements within one system has a stochastic
direction, and also in some cases assumes an odd nature.
2 Materials and methods
The article uses intelligent systems to solve problems of modeling and building in-
formation telecommunication systems and describes their state using logical-
mathematical dependencies. They will be called agent-oriented systems in the future
[8].
In the real world, when information systems are paramount, and therefore the role
of the person is significantly abolished, information intelligence agents come to the
fore. To solve the resulting problems, these agents are combined into groups, which
also allows them to best adapt to new environmental conditions.
Information intelligent agents are widely found in the works of domestic and for-
eign authors [9-10], [11-14], who developed and built models of neural fuzzy intellec-
tual agents. In conditions of very small discrete states, the models of intelligent agents
considered in these research projects showed optimal fundamental and applied results.
Error correction method, error signal feedback correction method, error backward
propagation method - all these modern neural network training methods were used in
these models.
However, the practical implementation of these models is still quite difficult to im-
plement due to the growth in geometric progression of process states. To simplify the
process of building such models, you need to consider multilevel neural models of
agents with fuzzy task conditions. Such models are also self-learning and adapt well
to the conditions of a booming heterogeneous information environment [15].
Over time, due to the reasons associated with an increase in modifications to in-
formation communication systems, a delay in determining the outcome of intelligent
agents, difficulties appear in managing neural models. All this makes us study uncer-
tainty in the behavior of intellectual agents, patterns in the development and self-
development of intellectual agents using fuzzy neural models.
Of interest is a number of studies describing technical systems of artificial origin
from the point of view of formalizing their processes of self-improvement and self-
organization. For example, modeling formal conditions of activity, where the main
function of phenomena is the function of adaptation [10]. In these works, the rules for
the phased development of processes are widely used. Models are built using logical
laws and using information technologies. Practical implementation of models is tested
on information manipulators or real robots. All this testifies to the accuracy and truth
of this scientific direction.
The basis of these works is an approach that describes phenomena from the point
of view of their biological component. The fixture function is well represented using
formalized criteria. Criteria are information technology rather than biological in na-
ture.
In addition, a new course arose aimed at considering the artificial origin of the
mind, which was called "Artificial Life". It appeared in the late 80s. XX century and
its goal was to describe and model self-organizational phenomena in biotechnical
systems.
The basis of this direction is work in which technical and genetic systems are de-
scribed using an NK-automatic scheme [12]. The circuit consists of n-components,
each of which is described by its own state, and has k inputs-outputs connected to
each other.
This direction is also very developed in the Russian Federation, the adherents of
which build and study biotechnical models of behavior of various forms of life.
These models are based on an algorithm for building artificial life, concentrating
on the phased development of simple organisms. These organisms are not involved in
the formation of mental models. Biotechnical models themselves are most often built
using computer software packages in a hardware or software technical environment.
As a result, the study of artificial systems is currently an urgent task. The study and
construction of such systems using neural models with fuzzy logic is especially in
demand.
3 Results
In the structure of an artificial system, there are a huge number of different modules
that can both depend on and not depend on other elements of the system. All are de-
signed to process, store and transmit information. When interacting with other ele-
ments of the system, modules can both influence and not affect the development of
the first. These modules act as intelligent agents and have all the necessary properties
to solve the agent-oriented problem.
Текст When describing the work of these intelligent agents, it turns out that all ex-
ternal influences that appear in the system in the form of signals, signs and signs can
be considered as certain messages and presented as functional logical dependencies.
This action is called the input language of the intelligent agent. Conversely, all actions
that the agent exerts on the external environment can also be described using func-
tional logical dependencies. They can also be generalized into the output language of
the intelligent agent [16].
The resulting functional dependencies can be converted by application packages
and other software methods into different physical signals. The types of signaling can
be different, both synchronous and asynchronous. Also suitable is the physical encod-
ing of the signal.
Further, based on the obtained dependencies, the architecture of the neural network
is built, which can take on both a clear and fuzzy character. Input and output signals
can be measured with a certain degree of accuracy and significantly simplified by
describing them with simple dependencies.
But if we consider modern information systems where it is necessary to optimize
behavior under environmental conditions, then it is possible to receive messages and
signals with a less clear value. By drawing an analogy, these can be variables whose
values are contained in fuzzy sets. Schematically, the input and output of intelligent
agent messages is shown in Fig. 1.
Fig. 1. Intelligent agent operation.
The intelligent agent proposed for consideration is responsible for the recognition
of both correct and incorrect input information and interaction with other agents.
Therefore, to create a universal model of interaction of a combined intelligent agent,
we use the well-known scheme of states of an intelligent agent in a multi-agent in-
formation system.
Imagine a theoretical-multiple model of an intelligent agent in the following form
LI SN , C A , K , AF - this is a tuple of relational relations, in which SN – the
system number, C A – attributes, K – agents associated with the subject, AF – the
function of the action [17].
Pointing and descriptive attributes will be C A {C , Cˆ } , composed of which are
the exact and fuzzy attribute values, respectively. In turn, for the exact value of the
attribute it will be true Ci NCi , SCi , VCi , and for the fuzzy one
Cˆ j NCˆ j , X j , j ( x ), x (t ) , where, NC i , NCˆ j - the considered properties; SC i -
defining set; VC i - value at some time moment t ; ( x ) - a membership function
with a domain of definition X j ; x ( t ) - an element from a set U corresponding to a
given time t .
Let us denote: { R, R } - a set of elements that reflect the nature of incoming mes-
sages; {T , T } - a set of messages outgoing from an agent. This allows translating the
state model of an intelligent agent into mathematical language.
Thus, for the mathematical model of an intelligent agent, sequential operations are
performed on the entered sets: {C , Cˆ } , { R, R } , {T , T } . The transition states in the
behavior model are specified in the set {S } by the following classes: the reception of
the elements of the set { R, R } and the impossibility of reception - { R, R } .
To find out the nature of the content of messages received by the agent and the
number of clear and fuzzy attribute values, we define a set of predicates
{Pr} (Pr1, Pr 2,..., Pr ) in the state model of an intelligent agent, which in first-
order logic constitute a set of admissible predicates.
It is possible to analyze complex conditions and relationships for incorrect input in-
formation using predicate theory and fuzzy logic, respectively, formulas F and F .
To formalize the predicate calculus, we compose the alphabet of the predicate calcu-
lus K AF : A ({C , Cˆ }, { R , R }, {T , T }, { S }, {Pr}, &, , (, ), , , @, , , 0) .
The alphabet for subject variables will have the form P ( p, q , f , hA) , p , q -
elements of sets { R, R } , {T , T } , respectively, that is, information at the input and
output, f - logical formulas of predicates Pr , as well as logic of fuzzy statements,
hC - properties of an intelligent agent hC {hC , hCˆ } . A
Let us compose the axioms of the calculus in the form:
Cx ( @ S 0 @ hC A (0) @ @ ) , where is - the absence of data on the state of
the variable, and hC A (0) {hC (0), hCˆ (0)} consists of arrays:
hC (0) NC1 , SC1 , VC1 (0) ; NC 2 , SC 2 , VC 2 (0) ; … NC n , SC n , VC n (0) ;
hCˆ (0) NCˆ 1 , X 1 , 1 ( x1 ), x1 (0) ; NCˆ 2 , X 2 , 2 ( x 2 ), x 2 (0) ; …,
NCˆ m , X m , m ( x m ), x m (0) . Here, at the initial operation time of the intelligent
agent, VC i (0) - is the exact value of the i - th attribute at time t 0 , x j (0) is the
fuzzy value of the j - th attribute at time t 0 .
To complete the construction of the predicate K AF calculus under consideration, we
assign the inference rules in the form of a scheme of axioms: Ri ( Rl , R k ) ,
Tj (Tp , Tq ) , Fj Fj ( Fe , Fr (Wr )) , where Wr - the value of the degree with which the
logical formula is true. Depending on the specified degree of truth, the fuzzy function
will be active, and, therefore, used to apply the rule [18].
The number of inference rules that satisfy these schemes for each action model is
diverse. For example, the laws that transfer elements from a state S 0 to the corre-
sponding states of the form 1 and 2 are determined by logical schemes
Ri , p @ S 0 @ hC (0) @ q @ f Ri p @ S 0 @ hC (0) @ q @ f
and . Note that
p @ S i @ hC ( Ri ) @ q , Ti @ f , Fi p @ S i @ hC ( Ri ) @ q , Ti @ f , Fi
the operator was introduced to denote the state of refusal in processing incoming
information Ri . In these schemes, it is likely that Ti and Fi ( Ti - messages
at the output, Fi - a formula) - this is convenient as a variant of minimizing the number
of outputting circuits [19].
The transition of states from type 1 to type 1, both with a truth table Fi and without
R p @ S @ hC @ q @ f
it, is described by the schemes i i
and
p @ S @ hC ( R ) @ q , T @ f , F
j i j j
R p @ S @ hC @ q @ f , F
i i i
. It must be borne in Fi mind that it is possible to clarify
p @ S @ hC ( R ) @ q , T @ f
j i j
the degree of truth of the negation, that is, to draw up a formula Fi . Then the verified
formula Fi will not be displayed. If an intelligent agent has an infinitely repeating task
execution period, then the formula required for use is again determined by the previous
R p @ S @ hC @ q @ f
schemes i i
.
p @ S @ hC ( R ) @ q , T @ f , F
j i j j
Without analyzing the formula Fi , the transition to a new state is described by the
R p @ S @ hC @ q @ f R p @ S @ hC @ q @ f , F
scheme i i
, and the scheme i i i
p @ S @ hC ( R ) @ q , T @ f , F
j i j j
p @ S @ hC ( R ) @ q @ f j i
is analyzed F . i
Return to state 1 from 2 characterizes structures of the form
p @ S @ hC @ q @ f p @ S @ hC @ q @ f , F
i
and i i
.
p @ S @ hC ( R ) @ q , T @ f , F
j i j j
p @ S @ hC ( S ) @ q , T @ f
j i j
p @ S @ hC @ q @ f
Logical structures i
and
p @ S @ hC ( S ) @ q , T @ f , F
j i j j
p @ S @ hC @ q @ f , F
i i
indicate a return from state 2 to the same state. It should
p @ S @ hC ( S ) @ q , T @ f
j i j
be noted that in the transition circuits, a stop is determined during the transition to a
state S where there is no case of a new output and, accordingly, a return to the origi-
j
nal state S occurs.
0
As can be seen from the schemes for the inference axioms, they define calculation
functions hC ( S ) { hC ( S ), hCˆ ( S )} that can be compared with the transformations of
i i i
attribute values VC ( t ) , VC ( t ) ,…, VC ( t ) and x ( t ) , x ( t ) , …, x ( t ) .
1 2 n 1 2 n
Using the above, it is possible to divide intelligent agents into types, for example,
such as active and passive, precise and fuzzy. Each of which can be primitive and non-
primitive, parametric and an agent with a shell [20].
4 Discussion
The developed approach to providing fuzzy logic methods in the development of
multi-agent systems is to help create a set of intelligent agents capable of self-
development and solve a common system problem, to organize effective interaction
between agents at different levels of the hierarchy of information systems.
To organize a clear structure of interaction between agents according to certain
rules, each intelligent agent is assigned a clear role based on its capabilities. And here
it is possible to use both canonical number systems and number systems of a higher
level. That is, the set of processed input information, objects and parameters can be
ordered using the theory of structural calculus.
The developed model of a fuzzy neural structure allows agents to evolve, accumu-
late information and skills when interacting with the external environment of informa-
tion, and increase their scope without including decentralized artificial intelligence.
In the algorithm for creating this model, the following roles of an intelligent agent
are used, characterized by the level of artificial intelligence and the way of behavior:
─ reflection - the presence of a response to the constant movement of the environ-
ment and information coming from other intelligent agents;
─ focus on existing knowledge - the further behavior of agents in achieving the goal
is based on the previously laid down knowledge about the environment and recog-
nition of the situation when making decisions;
─ goal-setting and self-learning - the ability to accumulate knowledge, having a large
amount of data in the form of a previously introduced base and a system of goals,
behavior patterns and algorithms in unclearly specified conditions [21].
Thus, the role of the possibility of formalizing the input data in a clear or fuzzy
way for building a functional model of a mixed type of the considered neural struc-
tures increases.
5 Conclusion
The indisputable advantage of agent-based models in solving technological and com-
mercial problems allows them to be used in information systems to improve models
of intelligent agents. This allows obtaining self-organizing and fault-tolerant intelli-
gent information systems.
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