case study: better stay connected… or not?
DESCRIPTION
Benefits and limits of distributed intelligence! wrt. ecological diversity in the environmentTRANSCRIPT
Nicolas Bredeche !Université Pierre et Marie Curie Institut des Systèmes Intelligents et de Robotique ISIR, UMR 7222 Paris, France [email protected]
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?
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Question !
What about adaptation to an open environment?
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, …
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Hypothesis !
Distributed adaptation can be beneficial !in « rich » (spatial) environments
Case study: is this hypothesis true or false?
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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Expected result !6
environment diversity
perfo
rman
ce
distributed (situated)
centralized
distributed (well-mixed)
Expected result !7
environment diversity
perfo
rman
ce
distributed (situated)
centralized
distributed (well-mixed)
?
?
?
?
?
?
Methods
[email protected]@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
[email protected]@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
[email protected]@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
Roborobo (C++) !12
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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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
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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