Poster
Reference
Leaders and followers: a design pattern for second-order emergence
PARHIZKAR, Mohammad
PARHIZKAR, Mohammad. Leaders and followers: a design pattern for second-order emergence. In: 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) , Umea, Sweden, 2019
Available at:
http://archive-ouverte.unige.ch/unige:135682
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i. vegetative ii. aggregation iii. slugs formationiv. slugs behaviours
a. First-order b. Second-order
a. b.
1 2 3
4
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Umeå, Sweden June, 2019
Mohammad Parhizkar
Centre Universitaire d’Informatique (CUI) University of Geneva, Switzerland Email: mohammad.parhizkar@unige.ch
Giovanna Di Marzo Serugendo Centre Universitaire d’Informatique (CUI) University of Geneva, Switzerland Email: giovanna.dimarzo@unige.ch
Salima Hassas LIRIS-CNRS
University Claude Bernard-Lyon1, France Email: salima.hassas@liris.cnrs.fr
- Centre Universitaire d’Informatique (CUI), Geneva, Switzerland
- LIRIS-CNRS Université Claude Bernard-Lyon1, France
Project’s website for the videos:https://www.unige.ch/cui/cas/research/dicty Short-term objectives
•
Understand, simulate and visualize the intercellular activities of individual cells (growth, division, movement, secretion, differentiation, etc.).•
Modeling and simulating the emergence of slug behaviors:-
Phototaxis-
Ammonia effect-
Merging of two small slugs-
The relationship between slug’s speed, age, length•
Appealing to inspire the engineering of swarm roboticsThis project is supported by the Swiss National Science Foundation (SNSF), Grant: Dicty Project - 205321 179023.
Acknowledgment
• Validation of all models and simulations with biologists.
• Improving the first-order and second-order models.
• Continue the work on stream breaking mechanism.
• Continue the work on slugs properties such as speed, age, size
• Cell-cell adhesion: considering the cells more than one discrete point, simulation of the gap between first-order and the second-order collective behaviors.
• Translating the models and mechanisms to swarms of Kilobots.
Future works
Publications
1.M. Parhizkar and G. D. M. Serugendo, Social Amoeba Dictyostelium discoideum as an Inspiration for Swarm Robotics, IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems, 2015 , USA . 2.M. Parhizkar and G. D. M. Serugendo, An Agent-Based Model for Collective Behaviors of Social Amoeba
Dictyostelium discoideum Morphogenesis: Aggregation Phase, SWARM- The 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics, 2017, Japan.
3.Mohammad Parhizkar and Giovanna Di Marzo Serugendo, Agent-based models for first- and second-order emergent collective behaviors of social amoeba Dictyostelium discoideum aggregation and migration phases, Artif. Life Robotics, 2018, Volume 23, Issue 4, pp. 498–507.
Leader and Follower Design Pattern
Name Leaders and Followers
Achieving higher-order collective group motion of indi- Problem vidual entities, through local interactions only. The different
groups or super-organisms themselves act like a swarm.
An emergent property triggers a behaviour change, some entities take new roles, i.e. leaders, other entities of the same group follow a leader. Entities know to which group Solution they belong and follow their respective leader. The design of the whole system consists in designing the behaviour of the leaders as a swarm-based (first-order emergence).
By following the leaders, the individual entities achieve second-order emergent emergence.
Individual entities belonging to the different groups. Diffe- Entities rentiation of the entities into leaders and followers. Each
group is formed of one or more leaders and followers.
During motion, followers entities take the place of the entities in front of them. Leaders release signals to provide the direction of movement. Leaders apply a voting process Dynamics to decide on the direction of movement. The behaviour
of the leaders in the different groups corresponds to a first- order behaviour (e.g. uniform distribution).
D. discoideumslugs formation and sensitivity to light/heat.
Known Cells in the tip change their behaviour, they lead the uses remaining cells. Behaviour of the cells in the tip (leaders)
is a ”traditional” swarm behaviour (first-order).
Other cells follow leaders.
TABLE I LEADERS ANDFOLLOWERSDESIGNPATTERN
(a2) (b2)
(c2) (d2)
{
4 (a1)
0 1 2 3 4
(b1) (c1)
{ { { {
3 2 1 0
Fig. 1. Block division and uniform distribution around light source
directional sensing and leading of the slug tip which is made by pre-stalk cells [11], [12]. In our current model, as shown on the top part ofFigure 1, each slug is made of five blocks.
Block zero (red dots), which consists of pre-stalk cells (also known as anterior or
supreme-leaderor tip of the slug) is the
leaderfor the Block 1 (blue dots, followers). Block 1 is the leader for Block 2 and so on. Each block consists of
25 cellsof the same cell type (a1). A
major voting processallows the blocks to decide on which direction to go (c1). In the case of the tip cells (c1), most of the cells observe that the light gradient is “UP” so they will move up. Therefore, the whole red block will relocate towards up. The different cells/blocks follow their predecessor (b1), and they take their places afterwards, inside a whole slug [12]. In each slug, the supreme-leader block becomes attracted to light/heat and also
applies a repulsion algorithm to avoid other slugs.
Our models are successful in reproducing the essential features of this phase, namely, leader signalling, phototaxis, merging and ammonia affect. The bottom part of
Figure 1shows a series of second-order emergent behaviour: attraction of the slugs to light/heat; merging of small slugs (orange and green slugs merge in
(a2)and
(b2)); and repulsion amongslugs to reach a uniform distribution around the light source
(c2)and(d2). Videos of simulations and biological validations can be found at the project’s web-page
1.
IV. C
ONCLUSIONThis paper discusses a novel design pattern, called “Leaders and Followers”, for reaching second-order emergence from in- dividual agents modelling, inspired by our agent-based model for the second-order behaviour ofD. discoideum. Current work involves: biological validation; modelling other phases of
D.discoideum
life cycle; describing the framework and method we developed to identify first- and second-order in
D. dis- coideum; and experimenting second-order emergent behaviourin swarm robotics. R
EFERENCES[1] Matthias Becker. A simulation study of mechanisms of group selection of the slime mold Dictyostelium discoideum. In2010 IEEE 14th International Conference on Intelligent Engineering Systems, pages 321–
326. IEEE, 2010.
[2] A. Bucchiarone, D. Furelos-Blanco, A. Jonsson, F. Khandokar, and M. M. Mourshed. Collective adaptation through concurrent planning:
the case of sustainable urban mobility. InProceedings of the 17th In- ternational Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018, page 18801882. In- ternational Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, USA / ACM, 2018.
[3] John Dallon, Wonhee Jang, and Richard H Gomer. Mathematically modelling the effects of counting factor in Dictyostelium discoideum.
Mathematical Medicine and Biology, 23(1):45–62, 2006.
[4] John C Dallon, Brittany Dalton, and Chelsea Malani. Understanding streaming in Dictyostelium discoideum: theory versus experiments.
Bulletin of mathematical biology, 73(7):1603–1626, 2011.
[5] J L Fernandez-Marquez, G Di Marzo Serugendo, S Montagna, M Viroli, and J L Arcos. Description and composition of bio-inspired design patterns: a complete overview.Natural Computing, pages 1–25, 2012.
[6] Serge Kernbach. From robot swarm to artificial organisms: Self- organization of structures, adaptivity and self-development.Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution, pages 5–25, 2010.
[7] Richard H Kessin. Dictyostelium: evolution, cell biology, and the development of multicellularity, volume 38. Cambridge, 2001.
[8] A F M Maree.From Pattern Formation to Morphogenesis. Multicellular Coordination in Dictyostelium discoideum. Utrecht, 2000. 152 p. PhD thesis, Tese de Doutorado, Departament of Theoretical Biology and Bioinformatics, 2000.
[9] Alex Mogilner. Mathematics of cell motility: have we got its number?
Journal of mathematical biology, 58(1-2):105–134, 2009.
[10] Mohammad Parhizkar and Giovanna Di Marzo Serugendo. So- cial Amoeba Dictyostelium Discoideum as an Inspiration for Swarm Robotics. InIEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2015, pages 162–163, 2015.
[11] Mohammad Parhizkar and Giovanna Di Marzo Serugendo. An Agent- Based Model for Collective Behaviors of Social Amoeba Dictyostelium discoideum Morphogenesis: Aggregation Phase. InInternational Con- ference on : SWARM’17, 2017.
[12] Mohammad Parhizkar and Giovanna Di Marzo Serugendo. Agent- based models for first- and second-order emergent collective behaviours of social amoeba Dictyostelium discoideum aggregation and migration phases.Artificial Life and Robotics-Springer, 23(4):498–507, 2018.
1https://www.unige.ch/cui/cas/research/dicty Bio-inspired engineering of artificial systems
•
From biological behavior to design patterns-
Swarm behavior: the first-order emergent collective behaviors-
Organism behavior: the second- higher-order emergent collective behaviors•
Investigate D. discoideum to define design patterns for higher-order emergence•
Application to artificial systems, e.g. swarm roboticsLong-term objectives
•
Understand, simulate aggregation and slug-formation phases of D. discoideum life cycle.•
Second and higher-order emergence of slug behaviors•
Design patterns for second-order emergence and engineering methods•
Implementation of the first and second order collective behaviors models with a swarm of small Kilobots.•Red arc: first part, first-order collective behaviors
•Blue arc: second part, second- order collective behaviors
Fig. 1: The motivation of the project
pst cells psp cells
(a) (b) (c) (d) (e) (f)
Fig. 3: Slug structure: anterior part (tip): PST cells (pre-stalk cells), posterior part: PSP cells (pre-spore cells)
Table1: Leader and Follower design pattern
time: 25 time: 29
time: 33 time: 35
stream breakup
c.
a. b.
d.
one stream
reattainment aggregation
Fig. 13: CPM model implementation Fig. 14: Slug movement: the relationship
between speed and length Fig. 15: Stream breaking during aggregation of D. discoideum Fig. 16: 50 Kilobots Fig 11- a: Kilobot experiment, the initiation (first frame)
Fig 11- b: Kilobot experiment (last frame)- We have two independent slugs.
Each slug has a leader and a group of followers.
Fig 12- a: Kilobot experiment, the initiation (first frame)
Fig 12- b: Kilobot experiment (last frame) We have two independent slugs.
The slugs are attracted to each other.
They can merge and make a one bigger slug.
Fig 11- c: The trajectories of 7 kilobots, per frame Fig 12- c: The trajectories of 8 kilobots, per frame We identified the “Leaders and Followers” design pattern relating to second- order collective behavior (See Table I). The main idea behind this design pattern is to activate first- order behavior in some of the agents, i.e. the leaders; by following their respective leaders, the followers bring the whole system to exhibit second-order emergent behavior. This design pattern allows us to reach second-order behavior, while modeling individual agents only. The pattern can be used for other collective behavior, besides motion, where agents follow or imitate the behavior of leaders, themselves individually guiding independent activities. This results in several groups of agents, each handling a different task, e.g. in search and rescue operations.
leader
leader
leader
leader
a dead cell, which has stopped after few minutes pst cells
psp cells
(a) (b) (c) (d) (e) (f)
Fig. 2. Formation of the pseudoplasmodium (slug): After aggregation, pre- stalk and pre-spore cells arrange themselves into a slug. They first make a mound(a, b), which elongates and finally tips over(c, d). Fig.(e)shows the cylindrical finger that falls over onto the substratum and migrates as a pseudoplasmodium(f). The pst (shown in red) cells position themselves in the anterior part and the psp cells (shown in blue) relocate themselves in the posterior part.
A. Slug’s speed during migration III. THEMODEL
A. Model: Regardless of Slug Age Tip and sheath the slug are responsible for the slug’s motility. To model the slug movement, we used the formulated equation of slug movement of Innouye’s work [5] and the results of Smith’s work [1]. There are some assumptions of slug movement in Innouye’s work [5]:
1) A slug does not change its speed during the movement.
2) Migrating velocity is important when the slug moves forward to the light and heat.
3) All cells move in same direction with same speed and have relatively same position.
4) Each cell moves actively with a constant motive force.
5) Each cell meets with an intrinsic resistance related to its speed
6) The cells in the tip of the slug and the sheath helps the cell to move forward.
7) There is no resistance from the sheath on the sides nor on the back of the slug
Also, in Innouye’s work [5] we can find an equation between the slug’s length, width and its velocity. They have extracted this formula by making a multiple regression analysis on the data of27different slugs.
1 v= 0.271
L+ 0.00751
!+ 0.26 (1)
Where,vdenotes the migrating velocity andL,!for length and width of the slug respectively. In this equation the coeffi- cient for width of the slug is so small, so it can be regarded as zero. Thus, we can conclude the formula based on the length of the slug:
1 v= 0.271
L+ 0.26 (2)
cell Cells type
Pre-stalk cells behaviors
Pre-spore cells
psp cells
pst cells If I am
surrounded just by pst cells
No
move to center (sorting)
cAMP
threshold? stop
follow the cAMP gradient
DIF threshold?
1. I am sensitive to light 2. I am releasing cAMP
1. It is not enough to lead the slug 2. It is not enough to second threshold 3. merge_flag = 1 (possible)
find other slugs with merge_flag= 1 Major voting process to find the direction
1. Blocks recruitment 2. Release cAMP
Under
I am not surrounded by pst cells anymore 1.move to center 2. merge_flag = 0
No
move collectively to the small slug direction
Yes
Major voting process to find the direction move collectively
to the light gradient distance to the source Close
Still far away 1. stop 2. kill cAMP END
Above Under
Above pacemaker
acts like Yes
END
merge with another slug 1
2 Velocity changes based on the slug length Velocity changes based
on the slug length
Fig. 3.The model of each self-organised cell: . This is a complete version of our previous workREF to our paper. Here, we have different migrating velocities for slugs, based on their length.
Fig. 3 illustrates the flowchart of decision making for each self-organised cell. In the beginning, each cell can be one of the three cell types: PST, PSP or Pacemaker. If it is a PSP cell, it will just follow the tip cAMP signal. But if it is a PST cell, it will follow the whole flowchart. In this model, we have two quorum sensing phenomena which are indicated by red circles (one and two).
Fig. 4.Linear relationship between migrating slug velocity and its length. Each point represents a slug.
B. Model: Considering the Slug Age In this model, velocity and length of the slug is a team work of both kind of the cells (PST and PSP).
IV. SIMULATION In our simulation,
V. RESULTS REFERENCES [1] Smith, E. and Williams, K.L. The age-dependent loss of cells from
the rear of a Dictyostelium discoideum slug is not tip controlled.
Development, 1981, pp.61-67.
[2] Coates JC, Harwood AJ. Cell-cell adhesion and signal transduction dur- ing Dictyostelium development. Journal of cell science, 2001, 114(24) , pp.4349-58.
[3] Brock DA, Gomer RH. A cell-counting factor regulating structure size in Dictyostelium. Genes & Development, 1999, pp. 1960-9.
Leaders and Followers: a Design Pattern for Second-Order Emergence
Fig. 4: D. discoideum, aggregation phase, streaming: branching and breaking
Research Domain and Motivation
Fig. 5: D. discoideum, slug formation phase, culmination phase: slugs movement toward the light source
Dictyostelium discoideum
Simulation and Implementation
a.1 a.2 a.3 a.4 a.5
b.1 b.2 b.3 b.4 b.5
c.1 c.2 c.3 c.4 c.5
Fig. 2: D. discoideum life cycleFig. 6-a: Simulation of the aggregation phase of D. discoideum: 3500 normal cells, 1 spontaneous centers
Fig. 6-b: Simulation of the aggregation phase of D. discoideum: 5000 normal cells, 2 spontaneous centers
a. b.
Fig. 6-c: Simulation of the aggregation phase of D. discoideum: 7500 normal cells, 3 spontaneous centers
Fig. 2: D. discoideum life cycle
(a) (b) (c) (d)
(e) (f) (g) (h)
a. b. c. d.
e. f. g. h.
i. j. k. l.
m. n. o. p.
Fig. 10: D. discoideum, first and second- order collective behaviors
D. discoideum life cycle properties 1.The simplicity of its life cycle always fascinates researchers to simulate the
whole development process or some of its important stages, cyclic adenosine monophosphate (cAMP).
2.The life cycle duration is relatively short, lasting only one or two days.
3.Cells movement, intercellular chemical signaling and the developmental process, which are applicable to the other search of studies.
4.The exhibit collective system has the plasticity ability in its development process.
In its life cycle, we can see horizontally and vertically self-organizing different developmental processes.
Fig. 9: Simulation of eight slugs movement: merging, ammonia effect, phototaxis, uniform distribution around the light source.
Fig. 7: How blocks follow each other in a slug. The red block is the leader and the blue one is the follower.
Fig. 8: Slug implementation, individual cells and major voting process
