123

1 INTRODUCTION

In nature, there is an intrinsic hidden harmony

inherent in it which is reflected in the procedures of

intellectual support (IS) of learning process and

onboard intellectual systems (IP) in the form of

mathematical laws and logical structures. It is this

harmony that makes it possible to explain many

physical phenomena and to predict the vessel’s

behavior in emergency situations using combinations

of observations, measurements, and mathematical

analysis.

The development of scientific understanding of the

problems of the evolutionary dynamics of a ship in

emergency situations, that a university graduate of

the navigational faculty has to study, requires a deep

understanding of the modeling and functional

analysis of the interpretation of the ship’s dynamics in

the behavior and control fields of modern catastrophe

theory, computer mathematics and artificial

intelligence (AI) [1 ] - [25]. The basis of this process is

the idealization of the ship’s dynamics in an

emergency situation based on the integrated

management of the learning process methods and

models of which provide a mathematical description

of dynamic processes within the framework of set-

theoretic interpretation of complex systems (Figure 1).

Learning control unit

Expert module

Trainee modul

Situation model

Interface

Cognitive model

Error

diagnostic

unit

Situation unit

Assessment unit

Simulation unit

Trainee

Figure 1. Intellectual support of navigators training process

by means of emergency control methods

Intellectual Technologies in the Field of Fundamental

ducation of Navigators

V. Bondarev, V. Volkogon, Y. Nechaev & P. Kovalishin

Baltic Fishing Fleet State Academy

, Kaliningrad, Russia

ABSTRACT: The problem of “reshaping” the fundamental education of navigators in the conditions of

intensive development of modern computer mathematics, intelligent technologies and high-performance

computing is considered. The main attention is paid to the formation of the information-educational

environment that provides intellectual support for the trainee. Examples of the use of intelligent technologies

that contribute to the organization of the learning process as a creative process of building knowledge are

presented.

http://www.transnav.eu

the

International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 14

Number 1

March 2020

DOI:

10.12716/1001.14.01.14

124

Homeostasis [11] is a functional state of the

emergency control system in which the maintenance

of dynamic constancy is maintained within the

permissible limits of vital functions and parameters of

the system during various changes in the external

environment. The ability of the operator to provide

the homeostasis with the quality of thinking in a

formalized knowledge space determines the human

potential at a specified time interval and the impact of

human potential on the current state of the vessel as

an object of control (OC) - the human factor.

The report discusses some of the most important

aspects of the problem of improving the training of

specialists using the IS system [12], [13], [15].

2 METHODS OF ARTIFICIAL INTELLIGENCE IN

THE SYSTEM OF TRAINING NAVIGATORS

The developed concept of modeling the behavior of

the vessel in the trainee’s IS system determines the

application of the theoretical principles of

interpretation of evolutionary dynamics in complex

systems. On the basis of conceptual decisions a

continuous process of control over the development

of an emergency situation is formalized - integration,

abstraction and generalization- that determine the

strategy of making management decisions.

The conceptual model of processing the flow of

information based on a multi-model complex (MMC)

of vessel's interaction interpretation in a complex

dynamic environment is:

( )

(

) ( ) ( ){ } ( )

RYWDSVMRRFFU

→××=

:,)(

, (1)

where U(F) – is the functional interaction space;

F(R

) – function, that determines the spaces of

behavior and control in the process of the situation

development; М(τ) – set of models for interpreting

the situation; V(S) – set of elements of the

visualization system; D(W) – set containing

procedures for generating solutions and generating

control actions; Y(R) – set that forms the rules of a

fuzzy formal system (FFS) of the learning process

management; τ∈[t

0,tk] – time interval for

implementation.

Algorithmic relation feedback in the conceptual

model (1) in the IS system is used to model the

formation of control actions in the process of

performing the proposed task. An important aspect of

new ideas, concepts and theoretical principles of the

learning model is universality. The implementation of

the problem of universality consists in the use of a

dynamic theory of catastrophes [10]. Within the

framework of such an interpretation the construction

of a ship’s behavior model is carried out by studying

the facts and phenomena of the interaction dynamics,

transition to situations generalization and planning

actions that provide modeling and software

implementation of computational urgent computing

technology (UC) [23]. The ideas of universality consist

in the study of various interpretations of emergency

situations, the scenarios of which, determine the

actual processes of functioning in the IS system.

Another aspect of universality is that the developed

models open up the possibility of an effective

combination of the achievements of intelligent

technologies and high-performance information

processing tools that indicate a close relationship

between the methodology that exists between the

theory of learning and methods for studying states

and phase transitions in complex systems.

Formation of conceptual solutions during the

operation of the IS system in the framework of

engineering systematology knowledge envisages the

inclusion of two interrelated phases of research of the

task: generating alternative concepts and choosing the

preferred alternative [10], [11].

The main direction of research in the IS system of

the learning process is the construction and

interpretation of MMC with direct and inverse mutual

relations as well as various environmental factors.

Such formalization provides the possibility of solving

direct (forecast) and inverse (control) tasks based on

various models, and greatly simplify the process of

designing multiscale models from separate blocks:

environmental models, behavioral models, interaction

models.

Interpretation of the interaction dynamics is

carried out within the configuration of the computing

complex presented in the form of graph-interpretation

[11]:

( ) ( ) ( )( )

,,,, CEAUEVFG

(2)

formalizing events (V (Е, U), describing actions in the

IS system, and conditions A (E, C) - in the form of a

logical description of the state of the vessel (Fig. 2).

INTERPRETING SYSTEM

Current situations

Actions in the system based

on the generated control

transformations.

Implementation - using the

principles of multi-mode

control system

Operating conditions

A logical description of the

state of the system based on

a formalized knowledge

system. Implementation -

within the framework of the

principle of competition and

analysis of alternatives

Figure 2. Evolutionary dynamics of IS interpreted system

As follows from this view, the control module of

the software package of the IS system provides

processing of the information flow associated with the

states of the interaction system and its description

conditions in a complex dynamic environment. The

decision-making support (DMS) in the IS system is

implemented within time interval [t

0,tk] which is

matched by a sequence of discrete events.

(

)

[

]

ttS

∈

(3)

formalized in the framework of the principle of

competition [9], [15].

As a quantitative measure of the “human factor”

the probability is taken that the decision maker (DM)

transforms the emergency situation into a normal

(regular) situation by searching for effective solutions

125

for the operation of the IS system. An effective

measure to reduce the "human factor" on the basis of

modern IS technologies are intelligent learning

systems (ILS) and simulators [15] with a virtual

reality system. Figure 3 shows the sequence of

operations in the operation of an intelligent simulator

based on the onboard intelligent system of a new

generation.

Figure 3. The flow of information during onboard IS

operation as an intelligent simulator

These technologies illustrate the modern approach

to the organization of IS methods reducing the role of

the "human factor" in making management decisions,

the support of which, is provided by the software

package and the system of controlled remote

experiment [2].

Thus, with the help of the considered intellectual

technologies in the trainee’s IS system, the operator’s

control activity is modeled to prevent and eliminate

emergency situations through the interaction of

natural and artificial intelligence in the DMS system

[1], [2]. The control actions developed by the DMS

system allow the interaction process to be carried out

by managing various types of resources (material,

energy, informational, psychological).

The development of theoretical principles for the

interpretation of complex processes and phenomena

in modern research and practical applications is

characterized by the integration of intelligent

technologies of the 21st century and high-

performance computing. Such integration provides a

solution to complex scientific and technical problems

in the face of uncertainty and incompleteness of the

initial information. The methodological aspects of

developing the IS system [15] are implemented on the

basis of conceptual solutions of the dynamic theory of

catastrophes [10]. This theory opens up the

possibilities of functional analysis and mathematical

logic in analytic and geometric interpretation of the

physical aspects of various applications in the spaces

of behavior and control, defined by the processes of

identification, approximation and prediction. Most

applications are based on the integration of

knowledge about the behavior of the studied

phenomena on the basis of conceptual models,

ontological principles and axiomatics of set theory.

The core of the IS system that supports the creative

process of forming a specialist is a learning model (Fig.

4).

COMPUTING SERVICES

А –external environment

В –vessel dynamics

CONTROL COMPONENT

Computing services management

Management of information

processing

Trainee

С – interaction dynamics

D – modeling of dynamic situations

Е – visualization of dynamic scenes

F – generation and analysis of alternatives

G – development of practical recommendations

Visualization of

modelling results

Received data storage

H – risk assessment of decisions

Figure 4. IS model: a - writing of intelligent modules; b -

computing services

The training model is based on information

processing methods using complex ontology, data

mining and revealing “hidden” knowledge (Data

Mining and Image Mining concepts [4]), alternatives

analysis methods and decision generation using Soft

Computing concepts [25], algorithms information

retrieval in structured environments, realized with the

help of domain ontology representation, neural

network technologies, multi-agent, symbolic and

cognitive modeling [9], [11]. The description of the

modules of the trainee IS presented in Fig.4a provides

a solution to the problems of modeling and

interpreting solutions in various practical

applications. The inclusion of new methods and

algorithms for the interpretation of information in the

form of additional modules of the IE creative learning

process will expand the functionality of ILS and will

not change its overall structure and concept of the use

of intelligent technologies. The transformation of

information in the IP modules is carried out within

the framework of a service-oriented architecture [21].

As it can be seen from Figure 4, the tasks of

information processing are the main content of the

five main areas of the use of intelligent technologies

and methods of modern computer mathematics,

implemented by the IS system of the trainee. The end

result of the operation of the IS system is to improve

the quality and shorten the terms of servicing trainees

with the help of software modules. As a result, the

quality of the services provided by the system is

improved, and the directions for the development of

the generated solutions and the multidimensional

analysis of the data obtained are defined using the

concept of “optimal instructor” [6]. The complex

solution of information processing tasks is a

126

combination of hardware and software of the IS

system for creating a dynamic and flexible

information infrastructure that realizes the

capabilities of ILS new generation.

3 FEATURES OF THE TRAINING MODEL IN THE

FORMATION OF A SPECIALIST IN THE FIELD

OF NAVIGATION

The features of the training model in the formation of

a specialist in the field of navigation will be

considered on the example of the functioning of the

training model that provides the IS of the creative

process. We focus on the practical implementation of

conceptual solutions provided in the process of

trainee’s interaction with the program DMS system.

Multi-criteria optimization. The task of multi-

criteria optimization is one of the difficult tasks when

choosing management decisions. The origins of this

task are connected with the famous task of L. Euler

about the seven Koenigsberg bridges (Fig. 5).

Figure 5. Fragment of the situation defining the L. Euler’s

task

The numbers in this figure indicate the numbers of

bridges that need to be bypassed in the absence of

repeated transitions. The solution to this problem is

associated with enormous computational difficulties,

which are defined as “combinatorial explosion”. The

trainee is offered a solution to this problem as applied

to the selection of the optimal ship control strategy

under specified external conditions based on the

genetic algorithm (GA) [11], Bellman – Zadeh

approach [3] and T. Saati’s hierarchy analysis method

[14] with the UC software package. Using the GA

allows you to reduce the task to the search mapping

( ) ( )

StrOptXX

→−

(4)

within the framework of the optimization structure:

( )

,,: XJRXJ

=→

(5)

where (Х

1 – ХN) are vector functions represented as a

given structure; J - target functionality; f:Y→ R -

criterial function; R is a real number characterizing

the specific outcome of the alternative; j (X) is a

function that implements the mapping Х → Y.

Figure 6. The sequence of actions for solving the problem of

choosing solutions for three alternatives

This condition allows the trainee to solve practical

problems of analyzing alternatives when choosing

preferred solutions for a given formalization of a

fuzzy interaction environment, which is especially

important in controlling the dynamics of a vessel

under conditions of intense icing and divergence of

vessels. As an example of working with the software

system, Figure 6 shows the sequence of actions for

solving the problem of choosing solutions for three

alternatives (the program allows up to 10

alternatives).

Multi-agent simulation of the process of

divergence of vessels. The problem of ships diverging

under conditions of intensive traffic flow (TF) is

considered on the basis of multi-agent modeling (Fig.

7) which allows, in the process of learning, to build

dynamically configurable interaction environments of

intelligent agents (IA) depending on the

characteristics of the TF structure.

As a model of the dynamics of the TF, the trainee

is offered a choice of combinations of MASs

depending on the characteristics of the interacting

vessels, including those with an IA leader and with

many agents operating in an integrated virtual

environment [2].

Figure 7. Modeling and visualization of traffic flow

127

This model of MAS allows you to combine certain

aspects of the behavior of interaction objects and has

the flexibility to account for various modifications of

the TF. The input data for the simulation of the MAS

is information on the models of the behavior of the TF

with the interaction parameters and control objectives

in space and time.

4 CONCLUSION

The implementation of the structural and functional

configuration of the IS software package is carried out

using fractal geometry and entropy analysis within

the framework of the information processing

paradigm in a multiprocessor computing

environment [9], [11].

The problem of "embedded intelligence" in the

synthesis of conceptual solutions and practical

applications of research problems "Intelligent

technologies of the twenty-first century" was

discussed during 2009 - 2015. at International

conferences and congresses, including the Forums for

the Development of Modern Society (Section "Science

and Education") in the USA (Washington, San

Francisco) and the UK (Cambridge, Oxford,

Edinburgh).

Expanding the function of "consciousness" and

modeling behavior is the most important evolutionary

task of the trainee. The great Plato said: “Thoughts

rule the world. A thought devoid of striving and

burning is barren”.

Thus, the purpose of this study is to discuss the

main directions of training of specialists in the field of

navigation on the basis of modern approaches to

controlling the dynamics of complex systems in the

framework of the modern theory of catastrophes,

intelligent technologies and high-performance

computing. Conceptual solutions for the

implementation of these problems are based on the

fundamental results formulated on the basis of the

concept of the minimum length of A.N. Kolmogorov's

description [5] within the framework of the

complexity theory [16], the principle of the bifurcation

control by N.N. Moiseyev [8], the theory of incorrect )

tasks of A.N. Tikhonov [17].

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