case study: better stay connected… or not?

Nicolas Bredeche !Université Pierre et Marie Curie Institut des Systèmes Intelligents et de Robotique ISIR, UMR 7222 Paris, France nicolas.bredeche@upmc.fr

FoCAS summer school (Crete), 23/6/2014

benefits and limits of distributed intelligence!wrt. ecological diversity in the environment

Case study: better stay connected… or not?

nicolas.bredeche@upmc.fr

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Question !

What about adaptation to an open environment?

nicolas.bredeche@upmc.fr

Open environments

• behaviors: generalists or specialists ?

• optimizer: centralized or distributed ?

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J.M. Turner, 1813

Applications: robots in the real world, video games, simulation, … internet of things, …

nicolas.bredeche@upmc.fr

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Hypothesis !

Distributed adaptation can be beneficial !in « rich » (spatial) environments

Case study: is this hypothesis true or false?

nicolas.bredeche@upmc.fr

Interaction between the population and the environment

• Very homogeneous environment • All can display the same behavior • Expected: centralized is best

• Very heterogeneous environment • Only specialist are allowed (e.g. limitations wrt. the metabolism) • Expected: distributed/specialist is best

• Inbetween • …?

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nicolas.bredeche@upmc.fr

Expected result !6

environment diversity

perfo

rman

ce

distributed (situated)

centralized

distributed (well-mixed)

nicolas.bredeche@upmc.fr

Expected result !7

environment diversity

perfo

rman

ce

distributed (situated)

centralized

distributed (well-mixed)

?

?

?

?

?

?

Methods

nicolas.bredeche@upmc.fr-nicolas.bredeche@isir.upmc.fr

Decoding Evaluation

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Initial Population"(random solutions)

Evaluation Selection Variations Replacement

desc

ript

ion fitness

continue stop end.

Evolutionary Computation with Robots

nicolas.bredeche@upmc.frnicolas.bredeche@isir.upmc.fr

Decoding Evaluation

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Initial Population"(random solutions)

Evaluation Selection Variations Replacement

desc

ript

ion fitness

continue stop end.

simulation setup!robots are situated in the environment!

no reset between generations

nicolas.bredeche@upmc.frnicolas.bredeche@isir.upmc.fr

Decoding Evaluation

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Initial Population"(random solutions)

Evaluation Selection Variations Replacement

desc

ript

ion fitness

continue stop end.

centralized vs. distributed!selection can be done wrt. robot location / behavior

nicolas.bredeche@upmc.fr

Roborobo (C++) !12

nicolas.bredeche@upmc.fr

Roadmap (tentative)

• Experimental setup : foraging ? • all agents in one environment, synchronized generation • mutation-only • selection schemes: ‣ global: (mu+lambda), (mu,lambda) ‣ local: (mu,1), (mu-1,1) (…?)

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• Guidelines • homogeneous vs. heterogeneous environment • enforced specialist vs. possible generalist ‣ e.g.: genetically-coded metabolic function forces specialists

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nicolas.bredeche@upmc.fr

Roadmap (tentative)

• Open questions • dispersion and lifetime? ‣ longer life means more dispersion (ie. converge to well-mixed)

‣ vanilla version: simulate well-mixed by randomizing partners

• selection scheme for global approach? ‣ elitist vs. non-elitist schemes

• cooperation based on relatedness? ‣ low dispersion may favor altruistic cooperation

• decentralized as a key to complementary skills ‣ « more than the sum of its parts »

‣ What happen if cooperation « create » more energy (e.g. energy merging)

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Wrapping up

nicolas.bredeche@upmc.fr

Wrapping up

• Important question • decentralized: a constraint, or a feature?

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• Possible audience for this contribution (if publication) ‣ biologists (limited dispersion as a winning strategy) ‣ robotics (on-line distributed learning can make things easier) ‣ general audience (distributed intelligence can be more creative)

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