VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD

AI for Autonomous Ships –

Challenges in Design and

Validation

ISSAV 2018

Eetu Heikkilä

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Autonomous ships - activities in VTT

Autonomous ship systems

Unmanned engine room

Situation awareness

Autonomous autopilot

Connectivity

Human factors of remote

and autonomous systems

Safety assessment

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Contents

AI technologies for autonomous shipping

Design & validation challenges

Methodological

Technical

Acknowledgements: Olli Saarela, Heli Helaakoski, Heikki Ailisto, Jussi

Martio

AI technologies

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Definitions - Autonomy

Autonomy is the ability of a system to

achieve operational goals in complex

domains by making decisions and executing

actions on behalf of or in cooperation with

humans. (NFA, 2012)

Target to increase productivity, cost

efficiency, and safety

Not only by reducing human work, but also

by enabling new business logic

Level Name

1 Human operated

2 Human assisted

3 Human delegated

4 Supervised

5 Mixed initiative

6 Fully autonomous

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Definitions - Artificial Intelligence

The Turing test: Can a person tell

which of the other parties is a machine

AI is a moving target

When a computer program is able to perform a task,

people start to consider the task “merely” computational,

not requiring actual intelligence after all.

”AI is all the software we don’t yet know how to write.”

More practically: AI is a collection of technologies facilitating “smart”

operation of machines and systems.

Conclusions and actions fitting the prevailing situation.

In many cases learning from data or experience.

Picture:

J.A. Sánchez Margallo,

Wikipedia

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It’s all about better utilization of

data

Intelligence Visualization

IntegrationInformationutilisation

Data acquisition

and storage

Data fusion and

validation

Ensure reliable and relevant data

from multiple sources

Easy-to-understand and

descriptive visualization

of complex data

Interactive methods for focusing

relevant parts of the data

Monitor and report in real-time

Predict for next best actions

Optimize logistics, energy

and raw materials

Expose new business

opportunities

Collect past and real-time data

Acquire essential data from different sources

Manage high volumes of varying data

Integrate analytics as a part of enterprise

IT systems and decision chains

Artificial intelligence methods for

business purposes

Application specific implementation of

algorithms and analysis

Appropriate tools for domain specific

applications

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Stages in AI development1. Weak AI, Narrow AI

Focused on one narrow task, e.g., some game or diagnosis of a particular disease

Very limited adaptability, e.g., if the rules of a game are changed even slightly …

All current AI applications are Weak AI

2. Multi-agent systems Interaction of several weak AI applications

The whole is larger than the sum of the parts

Being developed, e.g., autonomous vehicles, virtual assistants (Apple's Siri, Amazon Alexa, …)

3. Strong AI, General AI Wide applicability and adaptability

Human-like consciousness

An evasive long-term research goal

4. Super AI Machine intelligence exceeds human intelligence

Singularity: AI develops even more powerful AI

Machines might take over.

Maybe some day (or some century)

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Machine learning

The bread and butter of the current AI boom, especially

Deep learning

Reinforcement learning

Supervised learning

Given 𝑥 and 𝑦 data, learn 𝑦 = 𝑓 𝑥 + 𝑒

Unsupervised learning

Given 𝑥 data, discover patterns in it

Clustering, dimensionality reduction,

anomaly detection, …

Simple 𝒇 𝒙

Complex 𝒇 𝒙

Statistical pattern recognition

Model identification

Artificial intelligence

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Deep learning Supervised learning with complex models

Especially large Artificial Neural Networks

Possibly millions of model parameters identified from data

Very good results in complex modelling

Nonlinear multivariate models

E.g., image classification

Downsides

Decisions cannot be well explained

Complex nonlinear models can behave strangely for some inputs

Image from Jeff Clune:

Deep Learning Overview

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Reinforcement learning Determine an action based on balancing

Exploitation of previous good choices

Exploration of possibilities not yet tried

Observe the result from the action

Global optimization that builds a model with possibly a large number of

parameters, e.g., a deep neural network.

Requires a large number of iterations

Games

AlphaGo playing against itself

Consumer analytics

“You may also be interested in …"

Simulation models instead of real processes

Random trials on real processes might be dangerous

Validity of the simulator?

Agent

Environment

Action Observation

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Many more techniques are called AI

… depending on task & model complexity …

Transfer learning

Adapt a large data set from a more-or-less similar task to supplement a

small data set available from a new task.

Reasoning

Rule-based systems, decision trees, case-based reasoning, …

Evolutionary computation

Genetic algorithms, … for challenging optimization tasks

Translation of natural languages

…

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Vision and natural language Pictures with 200 categories, e.g. “ant”

Answering natural language questions based on

pictures

Speech recognition from telephone calls

535.43 536.44 A: they think lunch is too long

536.67 537.28 B: {laugh}

537.33 541.56 A: so they're going to have like %uh thirty

minutes for each period and they're going to extend the

periods we're going to have more periods

542.24 543.15 B: oh God

Y. Shoham et al: AI Index, November 2017

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Uses for AI in autonomous ships

Situational awareness

Surroundings

Ship systems

Decision-making

Route planning

Navigational decisions

Design & validation

challenges

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Autonomy and AI vs. safety

Introduction of new technologies and

ways of working brings along new and

modified safety risks

Increasing system complexity

New interactions between humans and

machines

Lack of prescriptive standards increases

the technology developers’ responsibility

for assuring safety

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Selected challenges in design & validation

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Concept design

Focus of development activities shifts towards

the early concept design phase

Quality of system description

Including operating environment, stakeholders,

interfaces

Concept of operations

Requirements management

Goals for the system performance

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Architecture & detailed design

Reliable handling of large data amounts

needs to be ensured

Planning of data usage to teach the

system

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Data quality issues are often realized

only at a very late stage

Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D. & Tufano, P. (2012). Analytics:

The real-world use of big data. IBM Global Services.

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Implementation & integration

How to ensure the

system learns the right

things?

W-model

Increasing need for

simulator testing

Transparency of

machine learning

Training

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Models can behave strangely for

some inputs

Distortions can be crafted to produce the desired erroneous outcome

Example from https://www.darpa.mil/about-us/darpa-perspective-on-ai

“Panda”

+ =

<1% distortion

“Gibbon”

(99.3% confidence)

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Verification & Validation

Lack of prescriptive standards

Technology developer increasingly

responsible for demonstrating the

safety

Goal-based approach used to link

safety evidence & system

requirements

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V&V methodology: Goal-based approach

Problem: How to create a

comprehensible link between the

safety goals and evidence?

System modeled as a structure of

safety goals

GSN argumentation modeling

language

G2

Goal

GOAL

G0

Top Goal

GOAL

Is solved by

G1.1

Sub-Goal

GOAL

G1.2

Sub-Goal

GOAL

G1

Goal

GOAL

Is solved by

Is solved by

Is solved by

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Operation

Change management

New operational logic,

increased human-machine

interaction

Conclusions

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Conclusions

AI technologies bring both opportunities and risks in the maritime sector

Robust process for V&V of AI systems is needed

Domain understanding needs to be incorporated in all stages of

development

TECHNOLOGY FOR BUSINESS

Contact information: eetu.heikkila@vtt.fi

+358 40 849 5790