This document discusses concepts of self-adaptive and self-organizing systems and ways to reconcile the two approaches. It describes key challenges in developing complex, large-scale autonomic software systems using models and engineering tools. Self-adaptive systems are discussed in terms of feedback loops within and between components. Self-organizing systems are described as exhibiting emergent behaviors from the bottom-up through peer interactions. Integrating self-organization patterns into large self-adaptive systems and controlling self-organizing subsystem behaviors by design are identified as important questions. The role of the environment in engineering desired self-organizing behaviors is highlighted using the example of roundabout traffic flow.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
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Simulation of complex systems: the case of crowds (Phd course - lesson 1/7)Giuseppe Vizzari
First lesson and introduction of the PhD course on "Computational approaches to Physical and Virtual Crowd Phenomena" - titled "Simulation of complex systems: the case of crowds"
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Simulation of complex systems: the case of crowds (Phd course - lesson 1/7)Giuseppe Vizzari
First lesson and introduction of the PhD course on "Computational approaches to Physical and Virtual Crowd Phenomena" - titled "Simulation of complex systems: the case of crowds"
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Given this particular context, power system simulation faces enormous challenges to adapt in order to satisfy simulation needs of both cyber-physical and sustainable system challenges. Such challenges will be highlighted during the talk.
There is, however, an opportunity for disruptive change in power system simulation technology emerging for the EU Smart Grid Mandate M/490, which requires "a set of consistent standards, which will support the information exchange (communication protocols and data models) and the integration of all users into the electric system operation." These regulatory aspects will be explained to highlight the importance of collaboration between the power system domain and computer system experts.
Open modeling and simulation standards may have a large role to play in the development of the European Smart Grid which will have to overcome challenges related to the design, operation and control of cyber-physical and sustainable electrical energy systems. To contribute to this role, the KTH SmarTS Lab research group has been applying the standardized Modelica language and the FMI standard for model exchange in order to couple the domain specific data exchange model (CIM) with the powerful and modern simulation technologies developed by the Modelica community. These efforts will be also discussed.
Describe the need to multitask in BBC (behavior-based control) syste.pdfeyewaregallery
Describe the need to multitask in BBC (behavior-based control) systems?
Solution
Behavior-based control employs a set of distributed, in-teracting modules, called behaviors that
collectively achieve the desired system-level behavior. To an ex-ternal observer, behaviors are
patterns of the robot’s activity emerging from interactions between the robot and its
environment. To a programmer, behaviors are control modules that cluster sets of constraints in
order to achieve and maintain a goal. Each behavior receives inputs from sensors and/or other
behaviors in the system, and provides outputs to the robot’s actuators or to other behaviors. Thus,
a behavior-based controller is a structured network of interacting behaviors, with no centralized
world representation or focus of control. In-stead, individual behaviors and networks of
behaviors maintain any state information and models.
The basic principles of behavior-based control can be summarized briefly as follows:
• Behaviors are implemented as control laws (some-times similar to those used in control
theory), either in software or hardware, as a processing element or as a procedure.
• Each behavior can take inputs from the robot’s sen-sors (e.g., proximity sensors, range
detectors, contact sensors, camera) and/or from other modules in the
system, and send outputs to the robot’s effectors (e.g., wheels, grippers, arm, speech) and/or to
other modules.
• Many different behaviors may independently re- ceive input from the same sensors and output
action commands to the same actuators.
• Behaviors are encoded to be relatively simple, and are added to the system incrementally.
• Behaviors (or subsets thereof) are executed con- currently, not sequentially, in order to exploit
parallelism and speed of computation, as well as the interaction dynamics among behaviors and
between behaviors and the environment.
The ability to improve performance over time and to reason about the world, in the context of a
chang-ing and dynamic environment, is an important area of research in situated robotics. Unlike
in classical ma-chine learning, where the goal is typically to optimize performance over a long
period of time, in situated learning the aim is to adapt relatively quickly, toward
attaining efficiency in the light of uncertainty. Models from biology are often considered, given
its proper- ties of learning directly from environmental feedback. Variations and adaptations of
machine learning, and in particular reinforcement learning, have been effectively applied to
behavior-based robots, which have demon- strated learning to walk [38.
, navigate and create topological maps, di-vide tasks, behave socially , and even identify
opponents and score goals in robot soc-
cer. Methods from artificial life, evolutionary computation/genetic algorithms, fuzzy logic,
vision and learning, multi-agent systems, and many other research areas continue to be actively
explored and applied to behavior-based robots as their role in ani.
Experiences with Collaborative System Architecture Development within a Joint...Obeo
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More information:
https://news.eis.uow.edu.au/event/trusted-autonomous-systems-as-system-of-systems/
Keep updated with future events: http://www.uoweis.co/events/category/smart-infrastructure-facility"
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Read more about The Open Group SA at http://www.opengroup.co.za/
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Describe the need to multitask in BBC (behavior-based control) syste.pdfeyewaregallery
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patterns of the robot’s activity emerging from interactions between the robot and its
environment. To a programmer, behaviors are control modules that cluster sets of constraints in
order to achieve and maintain a goal. Each behavior receives inputs from sensors and/or other
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world representation or focus of control. In-stead, individual behaviors and networks of
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theory), either in software or hardware, as a processing element or as a procedure.
• Each behavior can take inputs from the robot’s sen-sors (e.g., proximity sensors, range
detectors, contact sensors, camera) and/or from other modules in the
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action commands to the same actuators.
• Behaviors are encoded to be relatively simple, and are added to the system incrementally.
• Behaviors (or subsets thereof) are executed con- currently, not sequentially, in order to exploit
parallelism and speed of computation, as well as the interaction dynamics among behaviors and
between behaviors and the environment.
The ability to improve performance over time and to reason about the world, in the context of a
chang-ing and dynamic environment, is an important area of research in situated robotics. Unlike
in classical ma-chine learning, where the goal is typically to optimize performance over a long
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7. Outline
• Self-‐adap)ve
systems
– Concepts
&
Experiences
• Self-‐organizing
systems
– Concepts
&
Experiences
• Reconcilia)on
– Roundabouts
and
more…
– Future
urban
scenarios
• Conclusions
8. Self-adaptive Systems: Concepts
• Key
research
issues
– From
my
own
very
personal
and
necessarily
limited
perspec)ve
• Engineering
the
structure
of
feedback
loops
– Individual
loops
– Nested
loops
– Interac)ng
loops
• And
the
components
within
– E.g.,
for
effectors
– Parameters
upda)ng
– Behavioral
upda)ng
(COP
&
C)
– Structural
upda)ng
9. The ASCENS Project
• ASCENS “Autonomic Service
Component Ensembles”
– EU FP7 FET IP
– Starting October 1st 2010, lasting
4 years
• Key Challenges
– How to develop complex and
large scale autonomic software
systems?
– Models + Engineering Tools
– Experience with swarm robotics
and car sharing of e-vehicles
10. The ASCENS SOTA Model
(IEEE ECBS 2012, IEEE WETICE 2012)
• Kind
of
conceptual
framework
• SOTA
::
“State
of
the
Affairs”
– A
Mul)dimensional
space
– Everything
in
the
world
in
which
the
system
lives
and
executes,
that
may
affect
its
behaviour
• Adap)ve
systems
can
(should?)
be
expressed
in
terms
of
“goals”
=
“states
of
the
affairs”
to
be
achieved
– Without
making
assump)on
on
the
actual
design
– It
is
a
requirements
engineering
ac)vity
11. SOTA: Components of the Model
• “State
of
the
Affairs”
::
S
(t)
at
)me
t,
of
a
specific
en)ty
e
is
a
tuple
of
n
si
values,
each
represen)ng
a
specific
aspect
of
the
current
situa)on
• Dynamics
::
evolu)on
of
Se
as
a
movement
in
a
virtual
n-‐dimensional
space
Se:
• Transi5ons
::
θ(t,
t
+
1)
expresses
a
movement
of
e
in
S
à
endogenous
or
exogenous
• Goal
::
achievement
of
a
given
state
of
the
affairs,
represented
as
a
confined
area
in
space
• U5lity
::
(constraints
on
the
trajectory
to
follow
in
the
phase
space
Se)
expressed
as
a
subspace
in
Se:
12. Using SOTA
in Analysis
• Model
checking
func)onal
and
non
func)onal
requirements
– Opera5onaliza5on
of
SOTA
goals
and
U)li)es
– Transforma5on
into
asynchronous
FLTL
– Verifica5on
with
LTSA
tool
• Elici)ng
“awareness”
requirements
– Iden5fica5on:
which
knowledge
(dimensions
of
SOTA
space)
with
components
– Virtualiza5on:
which
(virtual)
sensors
available
with
components
– Metrifica5on:
which
granularity/accuracy
is
needed
for
sensors
13. Using SOTA in Design
• Express
adapta)on
pa]erns
in
terms
of
G
and
U
– G
and
U
express
the
adapta)on
needs
– And
can
thus
drive
the
iden)fica)on
of
With
which
so_ware
architecture
structure
of
feedback
loops)
• At
the
level
of
both
individual
and
ensembles
– Two
levels
are
strictly
inter-‐twined
– And
possibly
self-‐expressing
the
structure
of
feedback
loops
• Pa]ern-‐based
approach
– What
general
structures
for
feedback
loops?
– Macro
taxonomy
of
pa]erns
14. SOTA Patterns
G
=
∅,
U
=
U1,U2,...
Un
G
=
G1,G2,...,Gm,
U
=
U1,U2,...,Un
GASC
=
GCSC
∩
GACM
UASC
=
UCSC
∩
UACM
15. Self-org vs Self-adaptive Patterns
• There are cases in which top-
down self-adaptive patterns are
more effective
– A small group of robots/vehicles
with a leader perceiving and
directing/negotiating the the group
– “Loci” of feedback control
• There are cases in which bottom
up self-org patterns are better
– A large group of robots/vehicles
works well with peer organization
– Self-organizing activities and
coordinated movements
– Distributed implicit control loops
16. Key Question #1
• How
to
integrate
bo]om-‐up
self-‐
organiza)on
pa]erns
into
large-‐scale
self-‐
adap)ve
systems?
– What
interface/API?
– For
what
classes
of
self-‐
org
behavior?
– What
mechanisms?
– Can
we
define
general
rules/approaches?
17. The SAPERE Project
• SAPERE “Self-aware Pervasive
Service Ecosystems”
– EU FP7 FET
– Starting October 1st 2010, lasting
3 years
• Key Challenges
– To define and implement a general
framework for self-organizing
service ecosystems
– Models + Middleware
– Experience with pervasive urban
services and pervasive displays
18. The SAPERE Approach
• Nature-inspired (Biochemical)
– Simply metaphor for combining/aggregating services
in a spontaneous way
– Whether human or
ICT ones
• Spatially-situated
– To match the
nature of urban
scenarios
– Inherently adaptive
– Spontaneous
reconfiguration of
activities and
interactions
19. The SAPERE Architecture
• Humans & ICT Devices
– Interact by injecting/
consuming service/data
components
• Service Components
– Execute in a sort virtual
“Spatial substrate”
– Distributed reactive tuple
space
– Moving, acting, composing, as
from eco-laws
• Eco Laws
– Rule local activities and
interactions
– Apply based on local state
– Self-organization of collective
behavior
20. Using SAPERE
• Inject “live semantic
annotations” (LSAs)
– messages+service
descriptions
• Eco-laws apply to LSAs
– Bonding (subsuming
discovery and composition)
between LSAs
– Propagation (pheromones
and fields) of LSAs
– Decay (evaporation)
• Observe resulting LSA
– Their content
– Their distributed structure
21. Example: Steering Mobility
• Mobile entities ingject LSA expressing their presence
– Propagation of LSA
• Observe other LSAs
– And if affected by their presence in chosing directions
23. Integrating Self-organization
and Self-Adaptation in SAPERE
• Some LSAs that bonds with each other but are insensitive to
fields and pheromones
– Autonomic manager can be easily integrated in the loop
• Other LSAs as fields and pheromones
– For self-org patterns
• All in the same environment/space and with same mechanisms
25. Key Question #2
• How
to
control
by
design
the
behavior
of
self-‐
organizing
(sub)systems?
– Predictable
non-‐
determinism
– Direct
engineering
of
self-‐organizing
behaviors
– E.g.,
in
SAPERE,
how
can
we
sure
that
the
macro
behavior
of
steered
will
not
diverge
from
what
expected
26. The Roundabout Lesson:
Engineering the environment
• The
shape
of
the
environment
can
affect
the
behavior
of
self-‐
organizing
components
– Without
undermining
their
autonomy
– Without
losing
the
advantages
of
self-‐organiza)on
– Yet
promo)ng
more
predictability
• And
enabling
top-‐down
engineering
– The
shape
you
give
is
the
behavior
you
get
27. Engineering the Environment
in SAPERE
• What does it means to “shape” the environment
– Shaping its perception by components
– Equivalent to the distort the way LSAs are perceived and propagate
• Very easy to
implement but…
– Still to be verified
its effectiveness
and the ease of
engineering top-
down behaviors in
this way
28. Engineering the Environment
in SAPERE
• What does it means to “shape” the environment
– Shaping its perception by components
– Equivalent to the distort the way LSAs are perceived and propagate
• Very easy to
implement but…
– Still to be verified
its effectiveness
and the ease of
engineering top-
down behaviors in
this way
29. Key Question #3
• Are
there
different
approaches
to
reconciliate?
– I
have
no
answers….
– However…
30. The Jazz Perspective
• A few “engineered” rules
– How and how not to interact
– Rythms and rules of interactions
• Freedom of self-organization for anything else
– With who and when to interact
– According to which internal goals/attitudes
– Dynamic instantiation of feedback loops
• Worth investigating? I have no idea but it is fascinating
– cfr “Ad-opera” approach
31. Key Question #4
• Where
will
reconcilia)on
approaches
be
firstly
applied?
– In
most
large-‐scale
so_ware
systems
– And
primarily
in
future
urban
socio-‐technical
superorganisms
32. Smart Cities: From Senseable…
• Sensing what’s
happening
– Via ICT devices Sense
– And social
networks
• To better
understand (via
data analysis) Understand
– City and social (compute)
dynamics
– At a global level
33. …To Actuable
• We can “shape”
other than
understand Sense
Act
(Steer)
– Actuating ICT
device
– Steering human
actions
• Closing loops that Understand
enables finalized (compute)
urban behaviors
possible
34. …To Actuable
• We can “shape”
other than
understand Sense
Act
(Steer)
– Actuating ICT
device
– Steering human
actions
• Closing the loop Understand
that enables (compute)
finalized urban
behaviors possible
35. Adaptation in Urban
Superorganisms
• The ICT and Human/Social level
blurred to the point of invisibility
– Their capabilities well complement
each other à high value co-creation
– High-levels of collective “urban”
intelligence
– Necessarily situated and adaptive
• Many levels of top-down and
bottom-up adaptation
– Centralized control (municipalities)
– Bottom up control (citizen
proactiveness)
– Hybrid (crowdsourcing)
• Will have to be orchestrated
36. Example:
Mobility in Urban Superorganisms
• Mobility per se :: steer for car, bike, ride sharing
• City maintainance :: please go there and do that
• Exhibitions ::steer to avoid crowd or suggest paths
• All of these require
– Sensing, computing (data interpretation) actuation (steering)
– Adaptive self-organized mobility strategies
– Top up engineering and control of behaviors
• Exxacerbating all previous engineering challenges
37. Conclusions
• Need to reconcile self-org and self-adapt approaches
• Integration of localized self-org sub-systems !
• Controlling self-organizing behaviors !
• Jazz ?
• Will be of fundamental importance in future urban
socio-technical superorganisms
• Yet there are still a lot of engineering challenges
• There included social issues à humans are back in the loop!