Case study: better stay connected… or not?
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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?
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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?
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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)
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Expected result !7
environment diversity
perfo
rman
ce
distributed (situated)
centralized
distributed (well-mixed)
?
?
?
?
?
?
Methods
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[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
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[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
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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) (…?)
!
• 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?
!
• 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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