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1 INTRODUCTION

The term “Artificial Intelligence” (AI) can be (quite

simplistically) approached as the effective simulation

of human intelligence processes by computer systems;

without the following list being viewed as complete, a

few indicative examples of AI include expert systems,

natural language processing and speech recognition

(used by voice assistants to “understand” and respond

to user commands), as well as machine vision (serving

the task of object/other vessel detection from nearfield

to horizon), among others [1]. Focusing on the financial

aspect of AI, an extended number of relevant

applications have already been employed by various

organizations around the world, with the aim to boost

their revenues by streamlining the related business

procedures, automating repetitive jobs, and improving

customer service. When the discussion is shifted to the

wider shipping industry, the so-called “Digitalization”

phenomenon -which also includes the topic of

Maritime Autonomous Surface Ships (MASS)- there is

a quite disruptive picture of how this industry may be

transformed in the near future [1-3].

The introduction into full service of a certain

number of MASS-type vessels (along with numerous

other advanced technology applications based on

digital means) provides a clear argument that shipping

has already entered a new era [3]. The rather simplistic

and at most times confusing term “Autonomous

Vessels” is often used to describe certain types of

modern ships (complex systems engaged in maritime

transport activities) that, to some extent, are able to

make decisions by themselves, requiring no human

input. This type of vessels is very heavily reliant on AI

applications for the conduct of safe navigation, obstacle

detection, and decision-making; the role of trustworthy

AI applications that can reliably serve the necessary

decision-making tasks needed is vital towards the

effective introduction of more MASS-type vessels in

full service [1-4]. In any case, safely and effectively

operating any ship under the MASS framework -

Artificial Intelligence (AI) Applications

and the Shipping Industry

D. Dalaklis

World Maritime University, Malmö, Sweden

ABSTRACT: Artificial Intelligence (AI) can be simply approached as the (effective) simulation of human

intelligence processes by computer systems. The issue of Maritime Autonomous Surface Ships (MASS), based on

support by numerous AI applications, is providing a quite disruptive picture of how the shipping industry may

be transformed in the future. After the necessary clarification of terms, a summary of certain important legal

developments in relation to the on-going introduction of MASS type vessels into full service is provided. The role

of trustworthy AI applications that can reliably serve the associated decision-making tasks is also discussed. In

the near future, the vast majority of maritime transport needs will continue to be served by those vessels termed

as “conventional” (regularly manned ships); the shipping industry is well known for its risk adverse behaviour

and a slow pace of adaptation towards this new operating paradigm is the most probable path of adoption.

http://www.transnav.eu

the International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 19

Number 1

March 2025

DOI: 10.12716/1001.19.01.38

326

whether it is controlled remotely, or, under a “fully

autonomous” classification- requires robust regulation

to ensure that nothing will go wrong therefore

protecting human lives, as well as cargo on board and

the vessel itself.

Over the course of time, the relationship between

humans and technology has always been associated

with continuous transformation [5]; however, history

also testifies that the relevant pace of change clearly

increased with the spread of steam power that set off

the notorious “Industrial Revolution” (IR), which is

related with wide-ranging technological discoveries,

particularly in the areas of agriculture, manufacturing,

mining, metallurgy, and transport, including off course

the very widespread application of the factory system

[6]. The IR (is a still on-going transformative process,

which) corresponds to a period of global transition of

the economy towards more widespread, efficient and

stable manufacturing. At its very beginning, water and

steam power made possible the expansion of factories,

replacing manual labour with machines. Furthermore,

a number of quite important developments followed

next (Fig. 1). Especially for shipping, it should be

highlighted that has benefitted via three main ways

during that early period: a) the introduction of steam

power on merchant ships (mechanization); b) an

enlarged volume of goods to be traded and demand for

raw materials, because of the increased factory output;

and c) certain metallurgical innovations that also

improved shipbuilding techniques [3, 6].

Figure 1. Different Stages of the Industrial Revolution and

Applications related to Industry 4.0 [1].

In summary, the first stage of the industrial

revolution involved a change from agrarian societies to

greater industrialization as a result of the steam engine;

the second stage of this evolutionary process (or,

second industrial revolution) was driven by electricity

and involved very significant expansion of industrial

production, because of the introduction of the

assembly line concept, as well as an extended number

of related technological advances (spanning over the

18th and 19th centuries, with a spill over to the early

20th century). Important innovators like Thomas

Edison and Henry Ford played crucial roles during

that era, introducing electricity to power factories and

revolutionizing mass production techniques [1,3].

Subsequently, the third stage of industrial revolution

(which is quite often termed as “digital revolution”),

involved the development of computers and

information technology (IT) (including the creation of

the Internet) during the second half of the 20th century.

A very important issue to highlight is the widespread

introduction of automation in various industrial

sectors (the shipping one also included) during this

period, which improved precision, efficiency, and the

ability to handle complex tasks, marking therefore a

significant leap forward in capabilities. Finally, the

current fourth stage of the industrial revolution is

considered as a “new era” rather than a continuation of

the third stage, because of fast and rapid developments

and the disruptiveness of associated technologies [3].

In other words, the term “fourth (stage of the)

industrial revolution” (4IR, or Industry 4.0) describes

the current and foreseeable environment in which

disruptive technologies and trends such as Cloud

Computing and the Internet of Things (IoT), Artificial

Intelligence (AI) and Big Data Analytics, Robotics,

Virtual and Augmented Reality (VR/AR) and/or

Simulation applications are all changing the way

humans live and work. [3, 7] All these technologies

(summarized with the help of Fig. 1) are working in the

same collaborative environment (interconnected, most

commonly via the Internet); this in turn allows various

computers, or even very complex systems (often

described as “machines”, or “system of systems” in the

wider literature) to easily communicate, analyse data

in real-time, and even make informed decisions (by

utilizing some form of AI application). For example, a

report under the title “Transport 2040: Automation,

Technology, Employment - The Future of Work”,

which was released a few years ago by the World

Maritime University (WMU), puts forward the notion

that: “Technological progress and innovation have

occurred throughout history and changed its course,

for example the Industrial Revolution in the eighteenth

and nineteenth centuries. Currently, we are about to

embrace what is now termed the Fourth Industrial

Revolution, which is characterized by the introduction

of artificial intelligence, robotics, more and more

interconnection, among other innovations” [8].

Especially for shipping, the multi-level impacts of

4IR have the potential to completely change the

contemporary framework of operations and associated

activities. A number of reports and academic research

efforts imply that the tasks of seafarers are expected to

be transformed into more digital ones, such as system

management and monitoring of operations, as well as

that their operational work may be decreased; statistics

also clearly indicate that more than 1,000 maritime

autonomous surface ships (MASS of varying

capabilities) are already operated by (more than) 50

organizations/companies throughout the world [4]. A

very indicative example of MASS-related vessel is the

construction project of MV Yara Birkeland, (already in

full operation) in Norway. This (zero-emission) vessel

transports mineral fertilizer from Yara's production

plant in Porsgrunn to the regional export port in

Brevik; it was put into commercial operation during

the spring of 2022. During the first two years of

operation, the vessel went through a gradual transition

towards a “full autonomous” mode of sailing [9]. This

initiative facilitated to substitute cargo transport

operated by trucks with a MASS-type container ship

between specific places for environmental and

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economic reasons. Therefore, it does not prevent other

traditional ships operating on the exact same route. At

the same time, it must be noted that the capacity of that

vessel is quite limited (3000-4000 TEU) as compared

with traditional container ships (20,000 TEU, or much

more). Finally, it is worth highlighting that this ship is

owned by the shipper itself, instead of a traditional

ship-owner.

Another “similar” example is the Autonomous

Spaceport Drone Ship (ASDS) that is utilized by

SpaceX company. The overall engineering approach is

to facilitate cost-efficient launching of space ships by

collecting and reusing the first stages of rockets after

launching, and ASDA captures the exhausted first

stage. Cutting a long way short, ASDSs can sustain

their positions autonomously, or by remote control

from another support ship when the rockets will be

landing. There is also another (quite similar) ship

(same concept), under the name A Shortfall of Gravitas

(ASOG), which does not require support by tug boats

anymore (Fig. 2) [3]. Like the previously mentioned

Yara Birkeland, ASDS has been developed to meet a

new need, which is to collect exhausted rockets, and it

will never replace conventional (or, as often called

“traditional”) ships. Thus, those MASS type ships that

were previously presented can be viewed as something

totally different from the vast majority of ships in

service today; for the time being they cannot easily

replace traditional ships (except, maybe for the tasks of

short-range domestic passenger ferries, which is

looking as a very appropriate business model by

considering that human intervention (in case

something goes wrong) is quite easy because of the

vicinity of the troubled vessel with the coastline).

However, it is also useful to highlight that work at

sea/seafaring is (very probably) expected to change in

the not-so-distant future; roles, organizational

structure and responsibilities will probably shift from

today’s normal way of working at sea (that involves

very long periods away from home) to an office type

profile that will mainly involve monitoring, managing

and supervising systems (from ashore).

Figure 2. The vessel “A Shortfall of Gravitas” [3].

Combining computer vision and deep learning

capabilities so that MASS type vessels can effectively

understand” their surrounding environment (therefore

achieving a complete situational awareness picture)

and safely navigate in close proximity with “regularly

manned” vessels is a very probable future. But, before

moving to a different direction, it is necessary to note

that certain MASS-type vessels are already working

alongside ordinary manned vessels with minimal

autonomous-specific regulation. With this lack of

guidance in mind, the International Maritime

Organization (IMO) has already presented the results

of its latest autonomous scoping exercise and

established a road map for autonomous regulation.

The IMO aims to integrate new and advancing

technologies in its regulatory framework -balancing

the benefits derived from new and advancing

technologies against safety and security concerns, the

impact on the environment and on international trade

facilitation, the potential costs to the industry, and their

impact on personnel, both on board and ashore. The

Organization wants to ensure that the regulatory

framework for Maritime Autonomous Surface Ships

(MASS) keeps pace with technological developments

that are rapidly evolving. Items under consideration

include remote control station related regulation;

determination of remote operators as mariners;

autonomous shipping SOLAS equipment

requirements; and regional-specific regulations [10,11].

It is a rather self-explanatory fact that automation and

the on-going digital transformation will fundamentally

change the shipping industry; the skills needed for the

next generation of deck and engineering officers will be

different than those required until now.

2 MASS TERMINOLOGY AND CLARIFICATIONS

RELATED TO AI APPLICATIONS

Etymologically, the term “technology” is derived by

the combination of the ancient Greek words “τέχνη”

(tékhnē), which meant “knowledge of how to make

things” and “λόγος” (logos) that described concepts

like “reason”, or “consideration” [5]. Much can be said

about “autonomy”, but perhaps etymology provides

the most appropriate method to approach the specific

term. It is also useful to remember that not only

technology, but also language is transformed as time is

passing by. In the older versions of the Greek language,

the work “αὐτονομία” (autonomia) is derived by

combining together the words “αὐτός” (auto) -by

having as a starting point in Ancient Greek “ἑαυτοῦ”

(heautoû), changed into Byzantine Greek towards

“ἑαυτός” (heautós) and then simplified even further-

which means “self” and the word “νόμος” (nomos),

meaning in turn “law” and resulting into “αὐτόνομος”

(autonomos); the later can be (combined) understood

to mean “one who gives oneself one's own law” [1].

This is why in developmental psychology and

moral, political, and bioethical philosophy, autonomy

is viewed as is the capacity to make informed and

uncoerced decisions. Shifting the discussion back

towards shipping, a certain number of publications

and reports utilize concepts like “autonomous vessels”

(or, “autonomous ships”), along with “uncrewed

vessels” and “unmanned ships” interchangeably. To

avoid confusion, it is clarified that an unmanned (or,

uncrewed) ship is simply a vessel without crew on-

board. Then, it is emphasized that the content of this

paper is relying solely on the IMO’s terminology in

relation to MASS. According to IMO, a Maritime

Autonomous Surface Ship (MASS) is defined as a ship

which, to a varying degree, can operate independently

of human interaction (and making AI applications a

necessity); computer and software can help to carry out

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various ship functions such as navigation, propulsion,

steering, and control of machinery. It is perhaps better

to use the term MASS type vessel and stay away from

the most times confusing term “Autonomous Vessel”

[1,3].

The wider maritime transport industry, which is

comprised by shipping companies and a very extended

number of related vessels (serving a literally huge

portfolio of maritime transport needs), as well as of a

really huge number of ports (with varying sizes and

capabilities), along with ship’ building companies

(shipyards, etc.) and various entities ashore (like ship

management commercial entities, maritime

administration designated authorities, port authorities,

among others) is already under the strong influence of

a digital transformation and is kind of self-fulfilling

prophecy that the utilisation of MASS type vessels will

continue to grow in the years to come. Technological

advances in sensors, electromechanical drives,

cameras, and satellite technology can help this type of

ships move across the seas and oceans of our planet

without human intervention. In the contemporary and

“digital-reliant” era, the correct combination of

hardware and software can effectively handle all tasks

related to ship operations. Indicative examples include

equipment status monitoring, engine control, cargo

control/loading, as well as docking and undocking,

which all can be performed by systems without a

human in the loop [1].

It has already been clearly emphasised that the

shipping industry’s operating paradigm is under the

influence of a digital transformation and it expected

that MASS-type vessels will expand their operations

along/in close vicinity of regular (or “regularly

manned”) ships (mixed traffic, or hybrid type

scenario). There is also no doubt that MASS level 4

ships will utilise advanced AI algorithms to safely

navigate the seas and oceans of our planet. More MASS

type vessels will be introduced into full service in the

near future of our planet and it is therefore not a

coincidence that according to a relevant Fortune

Business Insights report [12]: The global autonomous

ships market size was valued at USD 5.61 billion in

2023 and is projected to grow [from USD 6.11 billion in

2024] to USD 12.25 billion by 2032, exhibiting a CAGR

of 9.1% during the forecast period. Asia Pacific

dominated the autonomous ship market with a market

share of 37.79% in 2023. Additionally, the related

projections for the years to come are summarized

below (Fig. 3).

Figure 3. Asia Pacific region (projected) MASS type vessels

market size [12].

Steering the discussion towards to topic of AI, this

term (AI) is very commonly utilised by the IT industry

and deals with the design and implementation of

computer systems that mimic elements of human

behaviour that imply (even elementary) intelligence:

learning, adaptability, drawing conclusions, contextual

understanding, problem-solving, etc. AI is a crossroads

between multiple sciences, such as computer science,

psychology, philosophy, neurology, linguistics, and

engineering, to synthesize intelligent behaviour, with

elements of reasoning, learning, and adaptation to the

environment, while usually applied on specially

designed computers [13]. Its ultimate scope is to create

a fully functional “thinking machine” that is

intelligent, has consciousness, has the ability to learn,

has free will and is ethical. The term is often be credited

to John McCarthy (1956); Alan Turing had a few years

earlier devised the notorious Turing test, as a way to

test the intelligent behaviour of a machine [1]. It is a

rather common knowledge that AI applications have

already helped numerous organizations around the

world to boost their revenues by streamlining the

related business procedures, automating repetitive

jobs, and improving customer service. Therefore, it is

not a coincidence that the financial impact of AI

utilisation and rather pompous terms like “How

generative AI will reshape the enterprise?” have

recently dominated the public sphere. It is very clear

that AI has the potential to lead to a massive

productivity boom –but, unfortunately that impact

(most likely) won’t be shared equally across economies

around the world.

AI applications can be divided into those relating to

symbolic artificial intelligence. This domain attempts

to simulate human intelligence algorithmically by

using high-level symbols and logical rules. There is

also sub-symbolic artificial intelligence that seeks to

reproduce human intelligence by using elementary

numerical models that synthesize inductive intelligent

behaviours with the sequential self-organization of

simpler structural components (“Behavioural artificial

intelligence”) and therefore simulating brain function

(“Computational intelligence”) or are simply based on

the application of statistical methodologies [13, 14]. On

the other hand, the so-called “conventional” AI

involves most commonly machine learning methods,

which are characterized by rigorous mathematical

algorithms and statistical methods of analysis and

divided into [15]: a) Experienced or specialized

systems (Expert systems), which implement

programmed logic routines, designed exclusively for a

specific task, to give a conclusion. To this end, large

amounts of known information are processed; b) Case-

based reasoning. The solution to a problem is based on

the previous solution of similar problems; c) Bayesian

networks. They are based on statistical analysis for

decision-making and are useful tools with knowledge

discovery since directed acyclic graphs allow

representing causal relations between variables; d)

Behaviour-based AI. This describes a method of

shredding the logical process and then manually

constructing the result.

Facilitating the execution of AI applications via

relevant computer systems can be approached as

learning through repetitive processes (configuration).

The specific learning process can be based on empirical

data and non-symbolic methods. This type of activity

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can be distinguished in: a) Artificial neural networks,

with very powerful pattern recognition capabilities.

These networks “simulate” the function of the neurons

of living beings; b) Fuzzy logic systems. They are based

on decision-making techniques under uncertainty.

Systems as such are based on the concept of partial

truth, where the truth value may range between

completely true and completely false; c) Evolutionary

computation. Their development arose from the study

of living organisms and relate to concepts such as

population, mutation, and natural selection (survival

of the fittest) to more accurately solve a problem. These

methods can be further distinguished into

evolutionary algorithms and swarm intelligence, such

as algorithms that simulate the behaviour of an ant

community.

Focusing mainly on Machine Learning (ML), it

should be clarified that it relates towards “helping” a

machine (computer system) to develop ways of

learning, analogous to how a human being is able to

learn; this can be further divided into: a) supervised

learning, b) unsupervised learning; c) supportive

learning. The complete analysis is outside the scope of

this paper, but in order to provide a few more

clarifications/details, the term Supervised Learning

describes the process where the algorithm constructs a

function that represents given inputs (set of training) in

known desired outputs, with the ultimate goal of

generalizing this function to inputs with unknown

output. It is used in problems like classification,

prediction and interpretation. Next, Unsupervised

Learning, is describing the situation where the

algorithm constructs a model for a set of inputs in the

form of observations without knowing the desired

outputs. It is most commonly used in problems like

Association Analysis and Clustering. Additionally,

there is Reinforcement Learning, which describes the

situation where the algorithm learns an action strategy

through direct interaction with the environment and

Ensemble methods, that describe the situation of using

the together (combine) the results from multiple

learning algorithms or different initial data to obtain a

better overall performance for the relevant computer

system [16].

3 SUMMARY AND CONCLUSIONS

Today, over four fifths of all trade in the world flows

through the seas and oceans; this includes the crucial

trade of food, energy, and other essential goods. The

Covid pandemic and on-going geopolitical pressures,

put the maritime transport industry at a crossroads,

with many forces at play reshaping the sector’s roles

and operating landscape; at the same time, maritime

logistics are becoming more dependent on digitalized

processes [17]. MASS type vessels can provide

shipowners/charterers with various economic,

environmental and risk-related benefits. These benefits

can include decreased voyage times, reduced fuel

consumption, and no need to expose crews at risk.

Voyage efficiency can benefit many aspects of the

industry: in addition to saving expenses, it can help

parties with (binding and nonbinding) efforts to reduce

emissions and adhere to already established

international standards on carbon reduction. Taking

advantage of the benefits associated with the fourth

stage of the industrial revolution (also, often termed as

the “Industry 4.0” in the wider literature) has already

become a priority for various shipping companies and

ports globally, as part of their further development.

AI applications are increasingly being utilised by

various stakeholders of the wider shipping industry to

improve the current state of affairs and promote

further development; indicative AI applications that

are already used in MASS type vessels, include

reinforcement learning (mainly for safe navigation

purposes), computer vision (for obstacle detection and

improving situational awareness, although in the case

of a collision/accident the question of liability remains

unclear), and last but not least predictive maintenance

algorithms (for reducing related maintenance costs)

[18, 19].

It is indicative the on-going introduction and

further expansion of automation and various cutting-

edge technologies within the wider shipping industry,

such as exploitation of Cloud Computing and the

Internet of Things (IoT) concepts, deploying Artificial

Intelligence (AI) and Big Data Analytics (the shipping

industry is constantly and very heavily dependent on

timely and accurate data to feed its overall and quite

complex logistical plans), utilizing Autonomous

Vehicles/Robotics and Access Control Sensors, as well

as integrating Virtual and Augmented Reality (VR/AR)

and/or Simulations in daily operational (or, related

training) activities, among others. All these

applications are clearly standing out as important

enablers that can substantially improve efficient

operations and related profits. It is self-explanatory fact

that the various advanced technology applications

related to 4IR (Industry 4.0) are exercising significant

influence upon the shipping industry. However, in the

next years to come the vast majority of maritime

transport needs will continue to be served by those

vessels termed as “conventional” (or, “regularly

manned ships”); the shipping industry is well known

for its risk adverse behaviour and a slow pace of

adaptation towards this new operating paradigm will

(most likely) be adopted.

It is also true that at any point of time the building

characteristics and equipment of ships are heavily

reliant on the vessels’ “intended” mission, and most

importantly, upon the technology applications

available to support these quite complex activities.

Modern ships are already very heavily equipped with

numerous technologically advanced systems and

highly automated. At the same time, the on- going

improvement and integration-interconnection of

electronics systems, as well as advances in automations

and robotics have created a new operating

environment for the shipping industry, with MASS

type vessels being capable of transitioning from

research tasks/efforts into full and effective service;

relevant opportunities are just waiting to be reaped. At

the same time, it is important to factor in that AI

applications have reached a certain maturity level, and

associated software can help to truly break down any

existing limitations and create a collaborative

environment suitable for people and “machines”,

including off course remotely controlled unmanned

vessels. In any case, gradually adopting, testing, and

re-evaluating the expected technological

breakthroughs can help to build the necessary

confidence for effective “human-machine” interaction.

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Only extremely effective and with response in real time

AI applications can pave the way towards autonomous

systems and eventually to fully unmanned (uncrewed)

ships.

Trustworthy AI applications that can reliably serve

the decision-making tasks needed for the safe conduct

of navigation (under all weather conditions and no

matter the complexity of surrounding traffic) are vital

towards the effective introduction of more MASS-

related vessels into full service. At the same time, it is

important to consider that the hardware element of the

numerous sensors onboard “conventional”

contemporary ships has already exhausted any room

of further improvement; the use of advanced software

applications and utilization of AI tools to improve

more the capabilities of the various already existing

systems used to support the conduct of navigation on-

board those vessels could be the best way forward.

Coming to an end, another conclusion clearly standing

out is that building, improving and running AI

applications requires immense computing power; a

Cloud-based architecture can offer that in a flexible and

easy “scalable” environment (at relatively low-cost and

without huge initial investments), at least at the early

stages of development; off course, when the issue of

security will become the top priority, different

modalities should be created to ensure that there is no

opportunity to mess up with the AI application(s).

Last, but not least, effective management of “Big Data”

and deploying the right analytical tools should be

approached as a prerequisite for AI; for example, the

exploitation of Big Data and the role of certain software

applications in accessing and managing large volume

of information are key factors for improving (and even

optimizing) the conduct of operations and effective

management related activities. In turn, AI applications

can provide the solution to process unstructured data

and derive useful insights from it.

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