by designing the agents - René Doursat

learning, evolution. Imports. ▫ observe ... true architecture: non-trivial, complicated morphology ... tip. 4. 2. 6. Multi-agent evolutionary development (evo-devo) ...
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4TH WORKSHOP ON CAUSALITY IN COMPLEX SYSTEMS DSTO, CSIRO (Australia), ONR, AFRL (US), ISC-PIF

Causing and influencing patterns

by designing the agents: Complex systems made simpler? René Doursat http://www.iscpif.fr/~doursat

Paris Ile-de-France

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From natural CS to designed CS (and back) ¾ The challenges of complex systems (CS) research Transfers ƒ among systems

CS science: understanding "natural" CS (spontaneously emergent, including human activity) Exports ƒ decentralization ƒ autonomy, homeostasis ƒ learning, evolution

Imports ƒ observe, model ƒ control, harness ƒ design, use

CS engineering: designing a new generation of "artificial" CS (harnessed & tamed, including nature) 4

Complex systems made simpler? (a) Genotypical / generative level Designing (evolving) the agents, not the system: Lessons from morphogenesis

→ Causality from micro to macro levels (b) Phenotypical / phenomenological level Describing the system, not the agents: Lessons from neural networks

→ Causality within the mesocopic level 5

(a) Genotypical / generative level Designing (evolving) the agents, not the system: Lessons from morphogenesis

→ Causality from micro to macro levels

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Systems that are self-organized and architectured the challenge for complex systems: integrate a true architecture

the challenge for complicated systems: integrate self-organization

free self-organization

deliberate design

designed self-organization / self-organized design

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Toward programmable self-organization ¾ Self-organized systems 9 9 9 9 9

a myriad of self-positioning agents collective order is not imposed from outside (only influenced) comes from purely local information & interaction around each agent no agent possesses the global map or goal of the system but every agent may contain all the rules that contribute to it

¾ Structured systems 9 true architecture: non-trivial, complicated morphology ƒ hierarchical, multi-scale: regions, parts, details, agents ƒ modular: reuse, quasi-repetition ƒ heterogeneous: differentiation & divergence in the repetition

9 random at the microscopic level, but reproducible (quasi deterministic) at the mesoscopic and macroscopic levels 8

Exemple of hybrid mesoscopic model patt1

¾ Recursive morphogenesis

div2

grad1

...

patt3

René Doursat, ALife XI (2008)

genotype

grad3

div1 grad2

div3

patt2 9

patt

grad

div

B3 W

I4

E

I6 B4

N

GSA : rc < re = 1