International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 5
Number 3
September 2011
Widely introduced on ships and land-based facilities,
navigational information systems are designed to as-
sist users in the decision-making process. Their main
task is collecting and presenting information neces-
sary for safe navigation. Currently available tech-
nologies give wider opportunities to assist naviga-
tors in interpretation of navigational situations and to
generate suggestions of possible solutions. This is
connected with the observed development of these
information systems in the direction of decision sup-
port systems. These systems are based on the use of
knowledge, which includes: the existing legal regu-
lations, procedures of conduct, principles of good
seamanship, navigational theories. The point is to
appropriately acquire that knowledge and create its
representation, which enables its efficient and effec-
tive use. The problem of decision support also ap-
plies to the interpretation of the navigational situa-
tion in accordance with the International Regulations
for Preventing Collisions at Sea (COLREGS). Due
to the fact that in certain areas local law may apply,
it is also important to take it into account. Assuming
that decision support also includes propositions of
solutions (e.g., manoeuvres), a knowledge base
should contain the principles of good seamanship.
The knowledge engineering is a branch of infor-
mation technology which deals with issues of
knowledge acquisition, representation and sharing.
The methods and tools of this new discipline open
way to the construction of systems facilitating the
interpretation of the sea route regulations and the
application of the principles of good seamanship.
The knowledge base built in this way can be used as
one of the expert system elements, which may be
part of a larger decision support system.
There are many methods of knowledge representa-
tion that can be used in the navigational decision
support process. The most important are:
decision trees, as a representation of possible
paths of decision-making depending on the exist-
ing and changing conditions;
logical rules, presented in a simple or complex
form, contain the premises and conclusions re-
sulting from them;
frames that are the base of object-oriented repre-
sentation of knowledge.
One of the main problems which appears during
the design of the knowledge base consists in choos-
ing the proper method of knowledge representation.
The decision to choose the proper ways of reasoning
is as important as the explanation method and future
expansion of knowledge. The selection of these
methods is strongly determined by the specificity of
the application field, therefore it is important to
identify assumptions for a knowledge base and fur-
ther for an expert system.
Knowledge Base in the Interpretation Process
of the Collision Regulations at Sea
P. Banas & M. Breitsprecher
Institute of Marine Technology, Maritime University of Szczecin, Szczecin, Poland
ABSTRACT: The article presents the problem of transforming knowledge contained in the provisions of the
International Regulations for Preventing Collisions at Sea, and the so called good seamanship in computer
applications. Some methods of knowledge representation in decision support in avoidance of collision situa-
tions are compared and examined. Acquisition, representation and sharing of knowledge are taken into con-
sideration from the viewpoint of supplementing the knowledge database and computational complexity.
The knowledge base should satisfy the following
ability to reproduce and verify the rules arising
from regulations contained in COLREGS;
possibility of supplementing the knowledge with
special regulations issued by competent authority;
opportunity to submit informal knowledge con-
tained in the principles of good seamanship;
open database that allows easy expansion of
simple presentation of the knowledge stored in
the database to make it understandable to people
who are not systems designers, and easy to verify.
The expert system using the above knowledge
base should satisfy the following requirements:
generation of explicit answers resulting from the
knowledge contained in the database;
interpretation of COLREGS regulations and spe-
cial rules;
generation of proposals of actions to be taken
arising from the principles of good seamanship;
explanation of the system response by quoting
relevant rules or principles.
An example of the implementation of COLREGS
is, designed at the Maritime University of Szczecin,
the knowledge base included in the Navigational
Decision Support System (NDSS) (Pietrzykowski et
al. 2009, Wołejsza 2005). The method of knowledge
representation used in this system has a form of de-
cision trees. They give a possibility of checking the
subsequent conditions and, on that basis, determin-
ing the response of the system. Their main disad-
vantage is the difficulty of extension, which may
cause considerable complexity of the system, and
thus the difficulties in the verification of correctness
and prolonged time to a response.
Knowledge representation based on logical rules
(rule-based knowledge base) is open to change, ena-
bles easy verification of accumulated knowledge and
makes it possible to offer explanations by simple
methods. Due to data security and the systems appli-
cation field, designers decided that the expert system
based on this knowledge base would not be a self-
learning system.
Knowledge bases are an integral part of decision
support systems. According to the theory of infor-
mation systems, it is possible to distinguish several
transaction systems, whose main task is to collect
full information from trustworthy sources and
through simple models to allow the basic analysis
and compilation;
management information systems, which aggre-
gate data from other systems, among them trans-
action systems; they use cross trend analysis us-
ing equation models, that operate on deterministic
data, but there may occur shortcomings and con-
decision support systems, which have an extend-
ed data analysis mechanism for probabilistic, of-
ten contradictory, incomplete and incorrect; when
they assist the decision making process, they use
knowledge bases, and optimization and simula-
tion models;
expert systems, built with the use of knowledge
and skills of people who are experts in the appro-
priate field, which are given as logic and heuristic
artificial intelligence systems, which use a wide
range of artificial intelligence methods to perform
modelling and analyses.
Because of the wide use of Decision Support Sys-
tems, in addition to the foregoing division, they can
be characterized by taking into consideration many
other factors and criteria. For example, the division
can be based on decision-making model or decision-
making process modelling (description, prescrip-
tion). The division by type of controlled system or
process comes down to the determination whether
the model used is deterministic, statistical or fuzzy.
The number of steps in decision-making process (in
one step or in n steps), the number decision-makers
involved in decision-making process (individual de-
cision or group decision) and the time factor (how
the time span is determined to take actions and deci-
sions) are further factors. The next factor character-
izing the decision support system is the manner it
works static, when it stops just after giving the an-
swer or dynamic system that at a given time dis-
cretization and at the occurrence of certain events
(inputs) operates continuously and appropriately
adapts to the existing conditions. Despite the divi-
sions and classifications, which in fact may reflect
many aspects, decision support systems, in general,
deal with data acquisition, convert information into
knowledge and generate answers (decisions) on the
basis of the knowledge they comprise.
This division of classes also shows the evolution
process of information systems from static to fully
dynamic, managing the models in decision support
systems (Pietrzykowski et al. 2007). It should also
be noted that expert systems currently being devel-
oped and artificial intelligence systems are ranked as
a subclass of decision support systems.
It is planned that the developed knowledge base
will be an element of the system considered as an
expert system. With its open formula, cooperation is
also possible with the systems that belong to other
A typical expert system could be described as struc-
ture presented below:
user interfacea module which is responsible for
interaction between the system and the user (nav-
igator). The module input allows user to ask ques-
tions and to specify additional information. The
module also gathers input data from other sys-
tems. The module output provides the user with
answers and explanations;
knowledge basedata which are characterised by
special structure adjusted to store logical rules.
The rules are quickly searched for in accordance
with given criteria;
inference mechanism main part of the system
that is responsible for the process of solving the
problem. To do so, the mechanism uses
knowledge base, logical rules and additional in-
explanation mechanism – part of the interface that
provides the explanation and gives legitimacy for
the system answer (decision) (Giarratano & Riley
2004, Jackson 1998).
The most important issues that shall be examined
during the process of creating knowledge base of the
expert system which offers COLREGS interpretation
and gives solutions for aiding navigational decisions
database structure definition;
data acquisition.
Because the expert system with rule knowledge
base is to be considered, specific problems that
should be worked out are as follows:
rules based on COLREGS and local laws or regu-
inference mechanisms that are supposed to seek
proper rules and formulate conclusions;
priorities mechanism that is responsible for build-
ing proper hierarchy of exact rules and regula-
working out the inference mechanism based on
good seamanship procedures, that works simulta-
neously with the mechanism that considers
COLREGS rules and local regulations.
The rules stored in the knowledge base are derived
from regulations provided in the COLREGS. The
regulations were taken under examination that led to
a set of logical rules. The connection between regu-
lations and rules was taken into consideration in
rulesnotation. It is essential during the explanation
process of the answer given by system. The opera-
tion of extracting data from special rules can be per-
formed in the same way.
In case of extracting any informal knowledge that
accounts for the principles of good seamanship, the
process is more complex. For example, Jones or
Cockroft diagrams which apply to restricted visibil-
ity situations can be treated as such source of
knowledge. The knowledge itself must be then
checked, verified and supplemented by experts -
The knowledge base and the system that are pro-
posed will contain a database divided into three parts
that apply to:
rules that concern the principles of good seaman-
additional rules that arise from special regula-
tions. It is important to bear in mind that local
rules have higher priority than general rules.
Therefore, it is possible that some conflicts will
occur and corresponding local and general rules
may be contradictory.
Ultimately, the proposed expert system is ex-
pected to work as stand-alone module or as be a part
of Navigational Decision Support System, devel-
oped at the Maritime University of Szczecin.
Figure 1. General architecture of navigational decision support
system on a seagoing vessel (Pietrzykowski & Uriasz 2009)
The diagram above shows arrangement of the
proposed system. It will replace the knowledge base
module and take over its function and tasks (Fig.1).
Due to the fact that the knowledge base will be
used for building the expert system and consequent-
ly it will operate in the decision support system, the
following structure of the expert system is taken into
consideration (Fig.2).
Figure 2. Structure of the proposed expert system.
The functions of the proposed expert system are:
Data acquisition the module that acquires data
required for the system to work, such as: position,
speed, navigational statuses, headings, relative
bearings, aspects, weather conditions, etc.;
Data analysis the module that analyses acquired
data, performs data standardization and calculates
formulas and parameters to be used in inference
COLREGS and special rules inference engine
inference mechanism which performs the inter-
pretation of knowledge contained in the rules
stored in knowledge bases; on the basis of data
sent from Data analysis module, optimal rules
are selectedthe rules that fit the examined situa-
tion. Then, the selected rules are used in forward
chaining process and, consequently, the navigator
is informed if the vessel is a give-way or stand-on
one. In addition, the rules and special rules which
are applicable at the moment will be presented.
Good seamanship inference engine inference
mechanism running in parallel to the above men-
tioned one, using a knowledge base containing
rules reflecting the principles of good seaman-
ship; diagram of operation is very similar to the
previous one, but the output gives a suggestion of
conduct in a particular situation; the operation of
this module is independent from the previous one,
so it is possible to give suggestions for appropri-
ate conduct in spite of an absence of priority such
as manoeuvres resulting from the Cockcroft dia-
gram for restricted visibility;
Knowledge bases set of knowledge bases con-
taining rules resulting from the extraction of
knowledge from COLREGS, local regulations
and the rules of good seamanship, supplemented
with links to relevant regulations and explana-
Presentation of results this module prepares the
information obtained from inference mechanisms
for presentation and transfers them to the user in-
Data needed to carry out the inference are derived
from a host computer or directly from the navigation
systems of the vessel. As assumed, the proposed sys-
tem always includes all information available. If
some data are required and are not among those
transmitted, it may be necessary to refine data, tak-
ing place via the master system. Obtained data are
processed in order to adapt them to the internal rep-
resentation of knowledge present in the system. This
processing is primarily responsible for standardiza-
tion and for searching for contradictions (e.g., in-
compatibility of data obtained from different
sources, taking into account malfunctions, measure-
ment error or human factor). In the proposed system,
input information is obtained in the form of vector
data and it may contain contradictions that must be
eliminate. The processed data are the basis for the
inference process.
Inference mechanisms work with dedicated
knowledge bases, which contain rules extracted from
the COLREGS, local regulations (special rules) and
rules of good seamanship, a transcript of the
knowledge of expert navigators, supplemented by
other sources of non-codified rules for determining
the proper conduct in different situations. The meth-
od of writing the rules was adapted to the transferred
input values obtained from the data analysis module,
enabling the user to find them promptly.
For the storage of rules there is a dedicated data-
base with appropriately designed structure. Defined
in this structure are the tables for each set of rules
and additional data used in the process for request-
ing and managing the work of the whole expert sys-
tem. The first step in the process of inference is per-
formed by a query to the appropriate tables, which
results in a set of rules satisfying the conditions
compatible with the existing navigational situation.
Then, the provided rules of inference will be carried
forward, resulting in the generation of a conclusion,
passed to the module output.
Because there exist different sets of rules for the
COLREGS (and local regulations) and the principles
of good seamanship, two conclusions are obtained in
parallel: on the right way with an indication of ap-
plicable regulation and rules, and the proposed pro-
cedure in the situation.
Figure 3. Functional diagram of data acquisition.
Figure 4. Functional diagram of the inference process.
The system operation is based on the following
algorithm shown in Figures 3, 4:
1 Get input from the user interface and navigation
1 Adapt to the requirements of the input search
2 Find rules matching the analysed situation in the
respective knowledge bases;
3 If rules were found in the database with local reg-
ulations, they must be taken into account in deci-
sion making;
4 If rules were found in COLREGS, determine their
hierarchy against the local regulations;
5 Decide on the basis of the rules contained in the
databases of local and COLREGS;
6 If emergency arises resulting from the absence of
rules and regulations of local COLREGS, acquire
again data (clarify) (go to point 1). If the data
cannot be refined, send information about the
lack of data required to make a decision;
7 If the base of the principles of good seamanship
contains rules for the situation, determine sugges-
tions resulting from these rules;
8 Show the results of the expert system work.
Marine navigation equipment and systems on
board are the source of input data for running the
expert system based on the knowledge base. Where
an ambiguous situation is identified, that may result
from the inability to establish certain facts, a request
will be made to supplement data, for example by a
dialogue between system and navigator.
Rules in the knowledge base should be supple-
mented with additional information, such as the de-
gree of validity of the a rules e.g. rules with the low-
est degree of validity are used only to carry out the
inference process, rules of higher degree will also
appear on the screen as a warning, and a rule having
the highest degree will additionally activate a sound
If the system has an additional knowledge base
that contains rules based on local laws, it is possible
to automatically take it into account when making a
decision. The moment of inclusion or exclusion de-
pends on the geographical position of the vessel ob-
tained from navigational devices (e.g. GPS).
Below are examples of the proposed expert system
functioning. The system receives selected input data
and provides a response in the form of conclusions
and explanations. The system response and risk of
collision which is determined by the decision sup-
port system provides the basis for working out the
manoeuvre according to COLREGS. The answer is
preceded by an analysis of the rules contained in the
knowledge base. Examples 1 to 4 are shown on fig-
ure 5.
1 Input data: restricted visibility = NO; Distance =
5Nm; Own priority (S1) = 6 (power-driven ves-
sel); Other vessel’s priority (S2) = 6; Relative
bearing = 005° PS - Port Side; Aspect = 150° SS -
Starboard Side; Own speed (V1) > Other vessel’s
speed (V2)
Conclusion: Give a way
Explanation: conclusion based on rule No. 13 of
2 Input data: restricted visibility = NO; Distance =
3Nm; Own priority = 6; Other vessel’s priority =
6; Relative bearing = 100° SS; Aspect = 040° PS;
Own speed < Other vessel’s speed
Conclusion: Give a way
Explanation: conclusion based on rule No. 15 of
COLREGS – Crossing situations.
3 Input data: restricted visibility = NO; Distance =
3Nm; Own priority = 6; Other vessel’s priority =
6; Relative bearing = 003° SS; Aspect = 004° PS;
Own speed Other vessel’s speed
Conclusion: Head on vessels, Alter your curse to
Explanation: conclusion based on rule No. 14 of
COLREGSHead-on situation.
4 Input data: restricted visibility = YES; Distance =
2Nm; Own priority = 6; Other vessel's priority =
6; Relative bearing = 010° SS; Aspect = 010° PS;
Own speed Other vessel's speed
Conclusion: all actions should be taken to avoid
collision – reduce your speed.
Explanation: conclusion based on rule No. 19 of
COLREGS (all actions should be taken to avoid
5 Input data: restricted visibility = NO; Distance =
10Nm; Own priority = 6; Other vessel’s priority =
6; Own speed ≈ Other vessel’s speed
Conclusion: none
Explanation: The distance is considered as safe.
Figure 5. Examples of navigational situations.
Explanatory notes and assumptions applied to the
above mentioned examples
8Nm is a distance assumed to be safe by the sys-
tem and no actions need to be taken in naviga-
tional situation;
Vessel’s priorities gradation is adopted from sim-
plified “privileged hierarchy(Rymarz 1995).
The construction of decision support systems can
significantly expand the capabilities of navigational
decision support systems. Their important element is
the knowledge base, which may be a separate struc-
ture or part of expert systems. The effectiveness of
decision support systems largely depends on the cor-
rectness of the knowledge base, its structure and
mode of action.
The following assumptions and proposals of im-
plementation have been presented:
1 To use an expert system as a sub module of deci-
sion support system;
2 To prepare a knowledge base based on rules;
3 To divide a database to 3 parts containing respec-
tively COLREGS rules, special rules and princi-
ples of good seamanship rules to improve effi-
ciency and to allow the knowledge to be updated.
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