1. Self-Organisation Programming:
a Functional Reactive Macro Approach
Roberto Casadei, Francesco Dente,
Gianluca Aguzzi, Danilo Pianini, Mirko Viroli
Department of Computer Science and Engineering
ALMA MATER STUDIORUM – Università of Bologna
June 21st, 2023
ACSOS’23, Toronto, Canada
https://www.slideshare.net/RobertoCasadei
R. Casadei Motivation Contribution Wrap-up References 1/16

2. Outline
1 Motivation
2 Contribution
3 Wrap-up

3. Context and Goals
building collective intelligence [1] in large-scale artificial systems
e.g.: swarms, edge-cloud infrastructures, crowds of wearable-augmented people
[1] R. Casadei, “Artificial Collective Intelligence Engineering: A Survey of Concepts and Perspectives,” Artificial Life,
Jul. 2023
R. Casadei Motivation Contribution Wrap-up References 2/16

4. A key problem: self-organisation engineering
how to drive the (emergence of the) self-organisation in a
collection of agents or devices?
[2] G. Aguzzi, R. Casadei, and M. Viroli, “Towards reinforcement learning-based aggregate computing,” in
COORDINATION, ser. LNCS, Springer, 2022
[3] R. Casadei, “Macroprogramming: Concepts, state of the art, and opportunities of macroscopic behaviour
modelling,” ACM Comput. Surv., no. 13s, 2023
[4] J. Beal, D. Pianini, and M. Viroli, “Aggregate programming for the internet of things,” IEEE Computer, no. 9, 2015
R. Casadei Motivation Contribution Wrap-up References 3/16

5. A key problem: self-organisation engineering
how to drive the (emergence of the) self-organisation in a
collection of agents or devices?
self-organisation engineering
(semi-)automatic approaches
MARL Program Synthesis [2] ...
“manual” approaches
node-centric
TOTA (reactive tuples)
...
macro-programming [3]
aggregate computing [4]
[2] G. Aguzzi, R. Casadei, and M. Viroli, “Towards reinforcement learning-based aggregate computing,” in
COORDINATION, ser. LNCS, Springer, 2022
[3] R. Casadei, “Macroprogramming: Concepts, state of the art, and opportunities of macroscopic behaviour
modelling,” ACM Comput. Surv., no. 13s, 2023
[4] J. Beal, D. Pianini, and M. Viroli, “Aggregate programming for the internet of things,” IEEE Computer, no. 9, 2015
R. Casadei Motivation Contribution Wrap-up References 3/16

6. SotA: Aggregate Computing (AC) (in 1 Slide)
Self-org-like computational model
structure: graph w/ local neighbourhoods
interaction: repeated msg exchange with neighbours
behaviour: repeated execution of async rounds of sense
– compute – (inter)act (program maps context to act)
R. Casadei Motivation Contribution Wrap-up References 4/16

7. SotA: Aggregate Computing (AC) (in 1 Slide)
Self-org-like computational model
structure: graph w/ local neighbourhoods
interaction: repeated msg exchange with neighbours
behaviour: repeated execution of async rounds of sense
– compute – (inter)act (program maps context to act)
formal model of executions: event structures
δ0
δ1
δ2
δ3
δ4
device
time
0
0 0
1 0
2 0
3 0
4
1
0 1
1 1
2 1
3 1
4 1
5
2
0 2
1 2
2 2
3
3
0 3
1 3
2 3
3 3
4 3
5
4
0 4
1 4
2
m
e
s
s
a
g
e
self-message
reboot
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8. SotA: Aggregate Computing (AC) (in 1 Slide)
Self-org-like computational model
structure: graph w/ local neighbourhoods
interaction: repeated msg exchange with neighbours
behaviour: repeated execution of async rounds of sense
– compute – (inter)act (program maps context to act)
abstraction: computational fields (dev/evt 7→ V)
formal core language: field calculus [5]
paradigm: functional, macro-programming
source destination
gradient distance
gradient
=
+
dilate
width
37
10
1 def channel(source: Boolean, destination:
2 Boolean, width: Double) =
3 dilate(gradient(source) + gradient(destination) =
4 distance(source, destination), width)
M. Viroli, J. Beal, F. Damiani, G. Audrito, R. Casadei, and D. Pianini, “From dis-
tributed coordination to field calculus and aggregate computing,” J. Log. Algebraic
Methods Program., 2019
formal model of executions: event structures
δ0
δ1
δ2
δ3
δ4
device
time
0
0 0
1 0
2 0
3 0
4
1
0 1
1 1
2 1
3 1
4 1
5
2
0 2
1 2
2 2
3
3
0 3
1 3
2 3
3 3
4 3
5
4
0 4
1 4
2
m
e
s
s
a
g
e
self-message
reboot
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9. SotA: Aggregate Computing (AC) (in 1 Slide)
Self-org-like computational model
structure: graph w/ local neighbourhoods
interaction: repeated msg exchange with neighbours
behaviour: repeated execution of async rounds of sense
– compute – (inter)act (program maps context to act)
abstraction: computational fields (dev/evt 7→ V)
formal core language: field calculus [5]
paradigm: functional, macro-programming
source destination
gradient distance
gradient
=
+
dilate
width
37
10
1 def channel(source: Boolean, destination:
2 Boolean, width: Double) =
3 dilate(gradient(source) + gradient(destination) =
4 distance(source, destination), width)
M. Viroli, J. Beal, F. Damiani, G. Audrito, R. Casadei, and D. Pianini, “From dis-
tributed coordination to field calculus and aggregate computing,” J. Log. Algebraic
Methods Program., 2019
formal model of executions: event structures
δ0
δ1
δ2
δ3
δ4
device
time
0
0 0
1 0
2 0
3 0
4
1
0 1
1 1
2 1
3 1
4 1
5
2
0 2
1 2
2 2
3
3
0 3
1 3
2 3
3 3
4 3
5
4
0 4
1 4
2
m
e
s
s
a
g
e
self-message
reboot
sensors
local functions
actuators
Application
Code
Developer
APIs
Field Calculus
Constructs
Resilient
Coordination
Operators
Device
Capabilities
functions rep
nbr
T
G
C
functions
communication state
Perception
Perception
summarize
average
regionMax
…
Action
Action State
State
Collective Behavior
Collective Behavior
distanceTo
broadcast
partition
…
timer
lowpass
recentTrue
…
collectivePerception
collectiveSummary
managementRegions
…
Crowd Management
Crowd Management
dangerousDensity crowdTracking
crowdWarning safeDispersal
restriction
self­stabilisation
J. Beal, D. Pianini, and M. Viroli, “Aggregate programming for the internet of
things,” IEEE Computer, no. 9, 2015
R. Casadei Motivation Contribution Wrap-up References 4/16

10. Motivation: combining strengths from SotA approaches
Feature TOTA aggregate computing
programming
approach , local-to-global - global-to-local
complexity
management “ modularity - compositionality
declarativeness
- high - high
scheduling ap-
proach - reactive , periodic (round-based)
scheduling
granularity - fine-grained , coarse-grained
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11. Motivation: combining strengths from SotA approaches
Feature TOTA aggregate com-
puting
FRASP
programming
approach , local-to-global - global-to-local - global-to-local
complexity
management “ modularity - compositionality - compositionality
declarativeness
- high - high - high
scheduling ap-
proach - reactive , periodic (round-
based)
- reactive
scheduling
granularity - fine-grained , coarse-grained - fine-grained
R. Casadei Motivation Contribution Wrap-up References 5/16

12. Outline
1 Motivation
2 Contribution
3 Wrap-up

13. FRASP
FRASP
is a
Functional Reactive Approach
to
Self-organisation Programming
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14. FRASP
FRASP
is a
Functional Reactive Approach
to
Self-organisation Programming
designed by interpreting the aggregate programming model by a (distributed) functional
reactive programming (FRP) perspective [6]
impl as a Scala DSL using Sodium FRP library
R. Casadei Motivation Contribution Wrap-up References 6/16

15. FRASP
FRASP
is a
Functional Reactive Approach
to
Self-organisation Programming
designed by interpreting the aggregate programming model by a (distributed) functional
reactive programming (FRP) perspective [6]
impl as a Scala DSL using Sodium FRP library
FRP in a nutshell
FRP provides abstractions to express and combine time-varying values into a
dependency graph
1 val v1 = /* ... */ ;
2 val v2 = /* ... */ ;
3 val v3 = v1 + v2 ; // v3 gets updated upon change of v1 or v2
R. Casadei Motivation Contribution Wrap-up References 6/16

16. FRASP
FRASP
is a
Functional Reactive Approach
to
Self-organisation Programming
designed by interpreting the aggregate programming model by a (distributed) functional
reactive programming (FRP) perspective [6]
impl as a Scala DSL using Sodium FRP library
FRP in a nutshell
FRP provides abstractions to express and combine time-varying values into a
dependency graph
1 val v1 = /* ... */ ;
2 val v2 = /* ... */ ;
3 val v3 = v1 + v2 ; // v3 gets updated upon change of v1 or v2
AC + FRP: intuition
1 val selforgSubRes1 = f(/* ... */);
2 val selforgSubRes2 = g(/* ... */);
3 val selforgOutput = h(selforgSubRes1, selforgSubRes2); // h re-eval'ed iff inputs change
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17. FRASP in a nutshell
Data types
Flow[T]: a reactive collective
sub-computation representing a
time-varying signal of Ts
­ distributed! each device get its own
“flow” for a single task; the system
behaviour/result for the task is given by
all these flows
NbrField[T]: a collection of data from
neighbours
Neighbouring sensors
1 def nbrRange(): Flow[NbrField[Double]] =
2 nbrSensor(nbrRange)
Stateful flow evolution
1 loop(0)(v = v + 1) // implicitly
throttling
mux: strict choice
1 mux(sensor(temperature) THRESHOLD) {
2 constant(hot)
3 } {
4 constant(normal)
5 }
branch: non-strict choice
1 branch(sensor(color) == red){
2 nbr(constant(1)).sum // run by reds
3 } {
4 nbr(constant(1)).sum // run by blues
5 }
lift: combining flows
1 lift(nbr(mid(),nbrRange()){ (nId,nDst) =
2 s${nId} is at distance ${nDst}
3 }
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18. Example: gradient
Code and graphical representation of execution https://youtu.be/3QIWfNq3yxU
1 def gradient(source: Flow[Boolean]): Flow[Double] =
2 loop(Double.PositiveInfinity) { g = {
3 mux(source) {
4 constant(0.0)
5 } {
6 lift(nbrRange(), nbr(g))(_ + _).withoutSelf.min
7 }
8 }
gradient: field of minimum distances from source
Notation
∠ blue shadow: source
∠ gray: obstacle (no gradient computation)
∠ hotter colours → lower distance to source
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19. Example: gradient
Evaluation: correctness + efficiency
0 100 200 300
time
0.0
0.2
0.4
0.6
0.8
1.0
#
messages
1e6 mode = round
0 100 200 300
time
mode = reactive
0 100 200 300
time
mode = throttle
throttle
0.0
0.125
0.2
0.5
1.0
(a) Gradient: messages
0 100 200 300
time
0
2
4
6
8
output
(mean)
mode = round
0 100 200 300
time
mode = reactive
0 100 200 300
time
mode = throttle
throttle
0.0
0.125
0.2
0.5
1.0
(b) Gradient output
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20. Example: self-healing channel
Code
source destination
gradient distance
gradient
=
+
dilate
width
37
10
1 def broadcast[T](source: Flow[Boolean], value: Flow[T]): Flow[T] =
2 // impl follows same scheme as gradient, using distance to choose a value
3
4 def distanceBetween(source: Flow[Boolean], destination: Flow[Boolean]): Flow[Double] =
5 broadcast(source, gradient(destination))
6
7 def channel(source: Flow[Boolean],
8 destination: Flow[Boolean],
9 width: Double): Flow[Boolean] =
10 lift(gradient(source), gradient(destination), distanceBetween(source, destination)) {
11 (distSource, distDest, distBetween) = distSource + distDest = distBetween + width
12 }
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21. Example: self-healing channel
Graphical representation of dependencies among reactive self-organising computations
source destination
gradient distance
gradient
=
+
dilate
width
37
10
Channel
gradient
(source)
gradient
(destination)
distanceBetween
source destination
Sub-
computations
Computation
Sensors
nbrRange
Input
Width
Platform
Local sensors
Neighbour data
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22. Example: self-healing channel
Reactive Dynamics https://youtu.be/j_JX5wW03-w
stabilised channel (connects source to destination via a path of devices)
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23. Example: self-healing channel
Reactive Dynamics https://youtu.be/j_JX5wW03-w
a new potential destination appears
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24. Example: self-healing channel
Reactive Dynamics https://youtu.be/j_JX5wW03-w
the channel gets recomputed
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25. Example: self-healing channel
Reactive Dynamics https://youtu.be/j_JX5wW03-w
the channel gets recomputed
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26. Example: self-healing channel
Reactive Dynamics https://youtu.be/j_JX5wW03-w
the channel gets recomputed
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27. Example: self-healing channel
Reactive Dynamics https://youtu.be/j_JX5wW03-w
the channel re-stabilises
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28. Example: self-healing channel
Evaluation (correctness + efficiency)
0 100 200 300
time
0
2
4
6
8
#
messages
1e5 mode = round
0 100 200 300
time
mode = reactive
0 100 200 300
time
mode = throttle
throttle
0.0
0.125
0.2
0.5
1.0
(c) Channel: messages
0 100 200 300
time
0
2
4
6
8
output
(mean)
mode = round
0 100 200 300
time
mode = reactive
0 100 200 300
time
mode = throttle
throttle
0.0
0.125
0.2
0.5
1.0
(d) Channel: output
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29. Outline
1 Motivation
2 Contribution
3 Wrap-up

30. Conclusion
FRASP (Functional Reactive Approach to Self-Org Programming) re-interprets the
aggregate programming model by a functional reactive programming (FRP) perspective
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31. Conclusion
FRASP (Functional Reactive Approach to Self-Org Programming) re-interprets the
aggregate programming model by a functional reactive programming (FRP) perspective
∠ and also provides an original flavour of distributed FRP
R. Casadei Motivation Contribution Wrap-up References 14/16

32. Conclusion
FRASP (Functional Reactive Approach to Self-Org Programming) re-interprets the
aggregate programming model by a functional reactive programming (FRP) perspective
∠ and also provides an original flavour of distributed FRP
Combines the benefits of existing approaches (cf. AC and TOTA)
∠ expressiveness and compositionality
∠ reactive execution (configurable)
∠ fine-grained reactive execution (not only the whole programs but parts of it)
R. Casadei Motivation Contribution Wrap-up References 14/16

33. Conclusion
FRASP (Functional Reactive Approach to Self-Org Programming) re-interprets the
aggregate programming model by a functional reactive programming (FRP) perspective
∠ and also provides an original flavour of distributed FRP
Combines the benefits of existing approaches (cf. AC and TOTA)
∠ expressiveness and compositionality
∠ reactive execution (configurable)
∠ fine-grained reactive execution (not only the whole programs but parts of it)
Future work
∠ libraries of reactive self-org blocks
∠ implementation of advanced self-org constructs like aggregate processes
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34. Thanks!
Channel
gradient
(source)
gradient
(destination)
distanceBetween
source destination
Sub-
computations
Computation
Sensors
nbrRange
Input
Width
Platform
Local sensors
Neighbour data
0 100 200 300
time
0
2
4
6
8
#
messages
1e5 mode = round
0 100 200 300
time
mode = reactive
0 100 200 300
time
mode = throttle
throttle
0.0
0.125
0.2
0.5
1.0
(e) Channel: messages
0 100 200 300
time
0
2
4
6
8
output
(mean)
mode = round
0 100 200 300
time
mode = reactive
0 100 200 300
time
mode = throttle
throttle
0.0
0.125
0.2
0.5
1.0
(f) Channel: output
Feature TOTA aggregate
computing
FRASP
programming
approach , local-to-
global
- global-to-
local
- global-to-
local
complexity
management “ modularity - composi-
tionality
- composi-
tionality
declarativeness
- high - high - high
scheduling ap-
proach - reactive , periodic
(round-based)
- reactive
scheduling
granularity - fine-grained , coarse-
grained
- fine-grained
R. Casadei Motivation Contribution Wrap-up References 15/16

35. References (1/1)
[1] R. Casadei, “Artificial Collective Intelligence Engineering: A Survey of Concepts and Perspectives,”
Artificial Life, pp. 1–35, Jul. 2023, ISSN: 1064-5462. DOI: 10.1162/artl_a_00408.
[2] G. Aguzzi, R. Casadei, and M. Viroli, “Towards reinforcement learning-based aggregate computing,”
in COORDINATION, ser. LNCS, vol. 13271, Springer, 2022, pp. 72–91. DOI:
10.1007/978-3-031-08143-9_5.
[3] R. Casadei, “Macroprogramming: Concepts, state of the art, and opportunities of macroscopic
behaviour modelling,” ACM Comput. Surv., vol. 55, no. 13s, 2023, ISSN: 0360-0300. DOI:
10.1145/3579353.
[4] J. Beal, D. Pianini, and M. Viroli, “Aggregate programming for the internet of things,” IEEE Computer,
vol. 48, no. 9, pp. 22–30, 2015. DOI: 10.1109/MC.2015.261.
[5] M. Viroli, J. Beal, F. Damiani, G. Audrito, R. Casadei, and D. Pianini, “From distributed coordination to
field calculus and aggregate computing,” J. Log. Algebraic Methods Program., vol. 109, 2019. DOI:
10.1016/j.jlamp.2019.100486.
[6] E. Bainomugisha, A. L. Carreton, T. V. Cutsem, S. Mostinckx, and W. D. Meuter, “A survey on reactive
programming,” ACM Comput. Surv., vol. 45, no. 4, 52:1–52:34, 2013. DOI:
10.1145/2501654.2501666.
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