Minutes Kick Off Meeting

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Evolutionary Task Force

Amsterdam, november 2-3 2011


Michèle Sebag, Marc Schoenauer, Gusz Eiben, Evert Haasdijk, Juergen Stradner, Anne van Rossum, Berend Weel, Florian Schlachter, Wenguo Liu, Jean-Marc Montanier, Remco Tukker, Nicolas Bredeche


  • description of agenda for each task-force (deadline: Friday, nov. 11th)
  • weekly executive summary (by Gusz)


  • Every Thursday
  • At 11:00 CET
  • Skype or Mumble?


  • individual control parameters INACTIVE/IGNORED

SUB-TASK FORCE #1 : morphogenesis control parameters

  • Explicit (UWE+UT?) vs. Implicit (Graz and TAO) representation of the shape
  • First step: explorative
  • Sub-Task Force: UWE, INRIA, (+ Ronny@Graz)
  • Deadline: NOV. 30

Remarks on the current state:

Ronny Wenguo INRIA
evolvable + - .
tested on robot ? + .

SUB-TASK FORCE #2 : organism control parameters

  • First step
    • CPG(evert)
    • CPG(florian)
    • AHHS
  • we forget, for the moment, about the joint AHHS-CPG architecture (AHHS as sensor info manager)
  • This sub-task force: berend, juergen, florian, anne
  • Deadline NOV. 30

Remarks on the current state:

AHHS CPG(evert) CPG(florian)
online-evolv. - + -
sensor inputs + - +
tested + +? +

SUB-TASK FORCE #3 ''internal reward (during lifetime learning)''

  • people: VU, INRIA, UT?
  • investigation of curiosity as internal reward.
  • Deadline: NOV. 30 for exploration

SUB-TASK FORCE #4 ''benchmark of simulator''

  • people: Anne/almende, Berend
  • Benchmarking the simulator
    • nb and size of organisms, nb of cells, test scale up
    • test #1: swarm of cells
    • test #2.a: organism, snake
    • test #2.b: organism, H-shape
    • test #3: swarm of organisms and cells (simulate the expected content of video)
  • Deadline: 16/11 (2 weeks from now)

Decisions for DAY 1

General considerations

  • general:
    • there are 4 stages: egg, growth, organism (ie. static body shape), dying.
    • only organisms transmit genomes
    • only eggs that are not aggregated can receive genomes
    • open question:
        • no fitness? environment-driven evolutionary adaptation (no fitness function / reproductive advantage)
        • implicit fitness? curiosity-driven, no ground truth
  • swarm-mode:
      • there will be no evolving swarm of single robots
          1. we’re skipping tasks evolutionary swarm mode: all robots are either an egg or in search of an organism to join.
      • use pre-defined behaviors (random walk, red light tracking, …)
  • organism-mode
    • we start with small organisms
    • development process in 2D; organism is expected to ‘stand up’ and move in 3D
    • there will be no evolving swarm of organism — no direct mating among organisms. The only mating is through organism planting seed in unused eggs.

Decisions for DAY 2

  • Agreement on scenario 1
    • parameters, general: how many cells/eggs, % eggs, size of arena, communication range, egg's timeout-to-restart, lifetime
    • parameters, in organism mode: time/trial (=tau), #trials, duration of learning wrt. lifetime
    • unlimited energy
    • walls
    • fixed nb of free cells and eggs
    • initial shapes
  • Agreement on various issues:
    • an egg re-becomes an egg after organism death
    • body and mind evolve together
    • mind may adapt during lifetime (eventually by evolution/learning)
    • mind may be trasmitted in a lamarckian fashion… or not.
        • Lexicon: organism = body + mind/brain = shape + controller
        • controller
        • evolutionary timescale
            1. control parameters (''narrow sense'')
            2. lifetime adaptation mechanisms
                1. init points (inherited vs. random)
                2. hyper-parameters
                3. internal reward
                    • options: (1) distance (2) traces (2.a) QI (2.b) evolved-QI
                    • start simple: distance, even though may not be correlated with survival
  • Conceptual boxes:
    • box #1: build candidate shape
    • box #2: build candidate controller
    • box #3: morphogenesis
    • box #4: epigenetics learning

What can be shown in the demonstration video / Requirements

  • in simulator:
      • video is ON for all experiments, we record EVERYTHING
      • record logs, for each time steps:
          • random seed for each log
          • compilation version(s) of everything (simulator, controller, etc.)
          • position of each cells
          • input and output values
          • for every new organism: record the description of shape and controller

Looking at the demo / visual outcome and impact expected:

  • observed:
      • number of shapes
      • size
      • distances
      • when
  • tools
      • clustering
      • genealogy
      • behavior as in physical trajectories
      • behavior as in sensory-motor space
      • behavior as in response test
  • monitoring
      • show the robot physical trajectories
      • datamining the log

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