Alexei Sharpanskykh science coffee

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Complex Adaptive Systems Theory
for Intelligent, Socially-aware Air Transport
Alexei Sharpanskykh

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Überlingen mid air collision

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Background
Assistant professor
at Air Transport and
Operations group
MSc in AI PhD in AI at VU Amsterdam
Focus on sociotechnical systems
Postdoc at VU
Complex adaptive
systems
VENI on safety
of air transport

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The modern air transport system is
open, complex, dynamic, nonlinear, stochastic
sociotechnical systems with many diverse actors

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Formal mathematical models in air transport
• Dominated by Operations Research models and
approaches
• Largely focus on optimization
• Passengers are often treated as resources
• Actors are largely simplified

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Social studies in air transport
• Empirical cases are dominant
• Human-in-the-loop experiments often focus on
local effects
• Mostly descriptive models, often context-specific
• Theories and models are not sufficiently precise
and detailed

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Social
systems
Technical
systems
Sociotechnical systems paradigm

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Complex Adaptive Systems

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Complex Adaptive Systems
Two essential modelling tools
Multiagent systems
Agent-based modelling and
simulation
Complex networks

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Multiagent systems (MAS)
Human1
Human3
Human2
System1
System4System2
System3
Accident
Human6
Human4 Human5
System5
Formal specification languages: hybrid dynamic temporal logics, hybrid
automata, …
Analysis by (rare event) Monte Carlo simulation and machine learning
techniques

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Emergence in crowd dynamics (1)
• Positive and negative emergence
Link: https://www.dropbox.com/s/qkfl9mg6axou86c/crowd.wmv?dl=0

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Emergence in crowd dynamics (2)
• Based on neurological, cognitive, and social models of emotional
decision making
• Personal devices are aware of human’s states
• Optimal penetration rate of personal devices = 76% (not 100%!)
https://www.dropbox.com/s/licj70vwfxcok1j/ecrowd.wmv?dl=0

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Coordination in MAS
Cooperative setting: several agents try to combine their efforts to accomplish as a group
what the individuals cannot
Competitive setting: several agents try to get what only some of them can have;
competitive agents try to maximize their own benefit at the expense of others

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Distributed control in airport surface movement
Long-term planning
by multiagent pathfinding (MAPF) algorithms
Short-term planning
by negotiation algorithms

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Distributed control of airport surface movement
at Schiphol
• Global information is not necessary for high performance
• The multiagent system handled the complexity of runway reconfiguration
better than humans
Collaboration with To70

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Collaboration with Vanderlande
Distributed control in airport luggage logistics

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Formation flying:
Coalition formation by negotiation
• Popular operational concept that requires little new technology
• Significant fuel savings: e.g., for formations of 4 w.r.t. current
flight schedules ~10% (~600 million dollars per year)

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Agent-based modelling of security, efficiency,
and resilience of airport terminal operations
The aim is to develop a holistic approach toward the airport terminal system,
taking into account all stakeholders that make up the airport ecosystem
Different aspects of the airport performance, such as security, efficiency, and
resilience are studied in relation to each other
Passenger behaviour, as well as employee behaviour are modelled explicitly
Decision making tools are developed to support airport managers in their
operational decision
Rotterdam-the Hague Airport is used as a living lab

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Airport simulation tool
collaboration with Rotterdam the Hague airport
https://www.dropbox.com/s/01mz390o78458fm/airport.wmv?dl=0

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Traditional Safety New Approach to Safety
Hazards
Losses
Downgraded
radarDual frequency
responsibility
Phone system
problems
ATCO missed
the conflict
Complex Adaptive Sociotechnical System
Risks, accidents, incidents
Monte Carlo
simulation
Swiss Cheese Model
Retrospective!

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Added value of CAS for design and analysis of
complex safety-critical sociotechnical systems
Both social and technical aspects can be modelled and analysed using
formal methods
Distributed (control) architectures are highly scalable, modular, and
computationally efficient
CAS Theory provides principles and methods for building highly flexible,
adaptive and resilient systems

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Challenges for CAS Theory
Methodological and mathematical basis needs to be further developed
Development of models, especially with social aspects may take long time
and requires (multidisciplinary) expertise
Integration of symbolic and statistical AI methods would further enhance
CAS Theory

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Contact
Dr. Alexei Sharpanskykh
Assistant Professor at Air Transport and Operations Group
Delft University of Technology, the Netherlands
Air Transport and Operations
Delft University of Technology
Faculty of Aerospace Engineering
Kluyverweg 1, office 4.09
2629 HS Delft
0031-15 278 1483
o.a.sharpanskykh@tudelft.nl