vtt technical research centre of finland ltd safety and... · acknowledgements: olli saarela, heli...
TRANSCRIPT
VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD
AI for Autonomous Ships –
Challenges in Design and
Validation
ISSAV 2018
Eetu Heikkilä
222/03/2018 2
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
322/03/2018 3
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