Evolutionary Task Force

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Minutes Converence Call, November 17 - 18 Nov 2011 10:48

Global Task:

• Each leader writes a paragraph on the progress before Sunday night.

Synchronize the experiments in each subtask so that people do comparable experiments

• Every group holds a private skype when necesarry
• Important thing is that experiments are comparable

Subtask 1: Morphogenesis

• Wenguo has improved his representation to include different connection rotations and heterogeneity
• Yao: done some experiments in its simulator; working with Ronny to integrate his findings.

Tasks:

• Michele: Before sunday evening, paragraph summarizing progress.
• Wenguo: Create mutation, crossover. He will send them to Christopher who will help make them good

Subtask2: Organism Control

• Until November 30 everyone can use their own simulator
• By December 15 the solution should be running in Robot3D
• We postpone the decision until December 15th, as we need to be able to compare them and for that they need to run in the same simulator
• Everyone needs to be careful that 2 weeks to port your code may be short, so you should look into porting it sooner.

Tasks:

• Juergen: Before sunday evening, paragraph summarizing progress.
  • Everyone: Send a short text (max 5 - 10 lines) on the progress you made

• Juergen has an implementation in his own simulator and will implement On-Line On-board evolution there first, and then move on to Robot3D
• Evert has an implementation in Wiibots and will first implement the inclusion of sensors there, and then move on to Robot3D
• Florian does not have his own simulator and will work in Robot3D immediately
• Yao has an implementation in his own simulator and will move toward Robot3D
• Ronny has an implementation in his own simulator

Subtask3: Internal Reward

• They will adapt an implementation of the QI to create a Static version of QI.

Tasks:

• Evert: Implement (he plan to do that before the week-end)
• There will be an internal discussion after the skype.
• Evert: Before sunday evening, paragraph summarizing progress.

Subtask 4: Simulator

• There are still some bugs that Lutz and Vojta are working on.
  • For instance the plugin threads are currently faulty
• The target platform for now will be Ubuntu 11.04.
  • Any bugs reported should be from this platform
• Report any bugs to Vojta
• We will organize a number of tutorials on using Robot3D
  • First tutorial will be somewhere next week.
  • Topics will include
    • Getting, Compiling and Running Robot3D
    • Where to write your controller code, and how to use it in the simulator
    • Some examples
• Libor and crew have created a component which measures how fast/slow the simulation is compared to real time
• Several scenarios have been conceptualized and will be implemented
• First results somewhere next week (Wednesday?)
• Libor will investigate whether a publication can be made on the simulator
  • The simulator may be a legacy of the project with desirable characteristics

Tasks:

• Berend: Before sunday evening, paragraph summarizing progress
• Berend: Confer with Anne and Vojta about bug-reporting and tracking!

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Minutes Converence Call - 10 Nov 2011 15:51

SUB-TASK 1: MORPHOGENESIS

Goal

To test the feasibility of the representation.

Spokesperson

• Michele (INRIA)

Partners

• Wenguo: explicit representation, task: create and test evolvability
  • • Christopher will help Wenguo with building operators for the explicit representation.
• Ronny: we have a genome which is evolvable and can build an organism. Tested in simulation.
  • • Yao will work with Ronny
• Inria, implicit representation, task: test evolvability

Other

What a good representation requires, depends on the size of the experiment/organism.

To bootstrap the evolution, we may need an alphabet of shapes; I, H, X, L, T.

• Ronny: A predefined alphabet may make it hard for evolution to find other shapes.

Initialization phase

For each representation we need to:

• Know which percentage of individuals is viable, when the genome is initialized randomly
• How long it takes to complete a shape
• Define crossover and mutation
• Know which percentage of offspring viable after crossover/mutation
  • • The means for:
    • Explicit: Is the expression well-formed?
    • Implicit: does it converge

The upper-bound on the size of organism is: 4-10.

Procedure

Everyone creates an experiment in their own simulator and sends their plan to Michele. Michele compiles this into a workplan?

SUB-TASK 2: ORGANISM CONTROL PARAMETERS

Goal

The goal is to compare CFG and AHHS and GRN (Yao-yao).

Task: walk, recognize the walls.

Spokeperson

• Juergen.

Partners

• Juergen (AHHS)
• Evert (CPG),
• Florian (CPG)
• Yao-yao (GRN)

Procedure

Evert sends an e-mail and organizes things.

SUB-TASK 3: INTERNAL REWARD

Goal

The goal is to re-calibrate the controller when a new shape is created; this recalibration is a nested optimization problem (on-line learning).

In order to optimize, we cannot use a external measure, so what is then criterion?
Options:

• Distance
• QI from the traces
• evolved QI (learning weights on the sensori-motor states) ** this might be too ambitious **

Spokesperson

• Evert

Partners

• Evert
• Michele
• Christopher

Procedure

Evert sends an e-mail and organizes discussion

SUB-TASK 4: SIMULATOR

Goal

Benchmark the simulator to get a feel for how large an experiment we can run in what time.

Spokesperson

• Berend

Partners

• Berend
• Anne
• Lutz
• Libor

Scenario

• Test#3: loose modules wandering, organisms moving randomly. Empty space with walls.
• Anne: appropriate sensors;

Other

Christopher: Do we keep all sensors on everywhere, or do we turn off sensors that are useless in simulator?

• For now we keep them all on, this is probably needed for AHHS anyway.

Berend: We will also simulate computational effort of crossover and mutation, to make the test as realistic as possible.

SCENARIO

• Procreating requires moving around.
• Egg receives 2 DNA; then becomes active and recruit others.
• A cell is an egg or a free cell.
  • • Whether a robot is an egg or a free cell is fixed at the start of the simulation
• An organism has a maximum lifetime (to be set later).
• An egg does not move

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Minutes Kick Off Meeting - 03 Nov 2011 16:21

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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