192
7 ALPHA:OK.
8 STATEMENT (CONFIRMATION (Alpha) is
TRUE)→S(H(A)isV)
9 ALPHA:CPAis0.7NmandTCPAis10
10 WARNING ((CPA(Alpha, Bravo) is 0.7) and
(TCPA(Alpha,Bravo)is 10))→ W((C(A, B) is 0.7)
andTC(A,B)is10))
11 ALPHA:Altercoursetostarboard
tokeep1.0Nm
asternofme.
12 DEMAND ((TURN(Bravo, Starboard) is TRUE)
and(DISTANCE(Alpha,Bravo)is1.0))→F((T(B,
R)isV)and(D(A,B)is1.0))
13 BRAVO: OK., I will alter course alter course15
degreestostarboard.
14 STATEMENT((Bravo) is TRUE) and
(COURSE(Bravo) is COURSE(Bravo)+15)) →
S((H(B)isV)and(G(B)is(G(B)+15))
15 ALPHA:OK.
16 STATEMENT(CONFIRMATION(Alpha)isTRUE)
→S(H(A)isV)
Thisform of representing certain procedures and
intentionswillenableusingthemindecisionsupport
systems. In addition, if it is presented in a readable
manner (graphical or digital), it could
be a valuable
supplement to verbal communication, allowing to
avoid ambiguity in expressing intentions and to
formally acknowledge intentions. Additionally in
ordertosuchdialoguehadplacetoappearnavigation
situation must be identify as requiring
communication. based on CPA, TCPA values and
inferencemethods theidentification canbeachieved
[11].
5 NON‐CRISPTERMS
Interpersonal communication in a natural language
makesuseofexpressionswithtermswhoseattributes
assumecrispornon‐crispvalues.Non‐crispvalues,or
precisely, the values of their attributes, may come
directly from a natural language, e.g.ʺnearʺ,ʺfarʺ,
ʺsafelyʺ,ʺdangerouslyʺ,
ʺaboutʺ,ʺsafe distanceʺ,
ʺdangerousdistanceʺ.Intheprocessofnavigation,i.e.
safeshipʹsproceedinginwater areafrompointAto
pointB,suchinformationattributesmaybegenerated
byshipboardequipmentanddetermineshipʹsstatus
as a state of a moving object in relation
to other
mobile or stationary objects. In the decision making
process the navigator‐operator accepts deviations
withintheassumedsafetylimits.Occurrenceofnon‐
crisp values appear ing in a specific communication
between human operators is a significant difficulty
for formal description of such communication. For
navigational communication ontology to offer
its
convenientuseinarealnavigationalenvironment,it
hastodescribebothcrispandnon‐crisp(fuzzy)terms.
Examples of non‐crisp terms can be found inthe
criteria for safety assessment, namely CPA (Closest
Point of Approach)andTCPA (Time to Closest Point of
Approach). This criteria
are commonly used in ship
encounter situations. Taking into account
uncertainties (inaccuracies) in assessing safety is
possiblewhenweuse,e.g.fuzzylogic,thatallowsto
describethesafetylevelwithlinguisticvaluessuchas
usedbyhumans.Thisconsistsinassigninga degree
of membership (x) 0, 1
to crisp values, e.g.
measured distance x. It means that, apart from
membership (1) or no membership (0) – as in the
classical set theory – membership maybe partial. In
caseofCPAitmeansthat,foravalueCPA
Lpresetby
thenavigator,an interval of tolerance is assumed to
exist CPA
Lmin, CPALmax such that
(CPA
LminCPALCPALmax), and any value of CPA is
assigned a degree of membership to the fuzzy set
CPA
LF, described by a membership function
(x) of
thisset(Fig.4)[9].
Figure4MembershipfunctionofafuzzysetfuzzyCPALF.
Similarly for a value TCPAL also preset by the
navigator,anintervaloftoleranceisassumedtoexist
CPA
Lmin, TCPALmax such that
(TCPA
LminCPALCPALmax), and a membership
functionν(x)(ν(x)0,1fordegreeofmembershipto
the fuzzy set TCPA
LF is described by analogous
function.
Thecriteriaofshipdomainandshipfuzzydomain
canbesimilarlyconsidered[8].
Theuseoffuzzylogic,methodsandtoolsoffuzzy
setsinparticular,enablesaformaldescriptionofnon‐
crisp terms, their inclusion in messages and their
interpretation and processing in
computer systems,
e.g.ininference processes.This descriptionrequires,
amongothers,thedefiningofrulesformathematical
notation of a message sent. The basic notation of
message(p)isasfollows:
XisR
whereXisavariable, is–sentence‐formingfunctor,
R–relationconstrainingthevariableX.
Dealing with a more extensive message, we can
additionallydefineawiderrangeofvaria bleoriginas
a function and write the message notation in this
form:
AX isR
where A is a group in the sub‐ontology to which X
belongs.
In a natural language there are various types of
sentences,e.g.affirmative(statement)orinterrogative
(question). For precise interpretation of message
content we can additionally adopt a function