Minutes Kick Off Meeting

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Evolutionary Task Force

Amsterdam, november 2-3 2011

people:

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

TODO:

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

WEEKLY NET MEETING: THURSDAY 11:00 CET.

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

SUB-TASK FORCES

• 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. 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. 1. control parameters (''narrow sense'')
           2. lifetime adaptation mechanisms
              1. 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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