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Robot Paintings Evolved Using Simulated Robots EvoMUSART ‘06 Gary R. Greenfield University of Richmond, USA Outline Motivation Background S-Robots Evolutionary Framework Assessment Parameters Evolved S-Robot Paintings On Autonomous Evaluation Conclusions Motivation

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robot paintings evolved using simulated robots

Robot Paintings Evolved Using Simulated Robots

EvoMUSART ‘06

Gary R. Greenfield

University of Richmond, USA

outline
Outline
  • Motivation
  • Background
  • S-Robots
  • Evolutionary Framework
  • Assessment Parameters
  • Evolved S-Robot Paintings
  • On Autonomous Evaluation
  • Conclusions
motivation
Motivation

“Artistic talent is far from a magic indefinable essence, possessed by a few and jinxed by deconstruction. Rather it can be thought of as an adaptive system, consisting of a particular updating scheme and low level local rules or techniques, which have been arrived at through an evolutionary process.”

-- Katie Bentley, GA’02 Generative Art Conference, Exploring aesthetic pattern formation, pp. 201-213.

j mccormack evomusart 05
J. McCormack: EvoMUSART ‘05
  • Open Problem #3: “To create EMA systems that produce art recognized by humans for its artisticcontribution (as opposed to any purely technical fetish or fascination).”
  • Open Problem #5: “To create artificial ecosystems where agents create and recognize their own creativity.”
  • My Observation: To recognize their creative efforts agents must be able to evaluate their creative efforts.
background
Background

-- V. Ramos and F. Almeida (2000), Artificial ant colonies in digital image habitats – a mass behavior effect study on pattern recognition.

-- L. Moura and H. Pereira (2002), Artistic Swarm Robots (ArtSBot).

-- N. Monmarche et al (2003), Interactive evolution of ant colony paintings.

-- G. Greenfield (2005), Evolutionary methods for ant colony paintings.

s robot design
S-Robot Design
  • Loosely modeled after Khepera robots

(Binary valued) proximity sensor

(Tristimulus) color sensor

s robot specifications
S-Robot Specifications
  • Center position (rx, ry).
  • Unit vector direction heading (dx, dy).
  • Three forward proximity sensors, and one rear proximity sensor, sensitive to a radial distance of 20 units.
  • Forward “field of vision” -90 deg. to +90 deg.
  • Rear “field of vision” -60 deg. to +60 deg.
  • All proximity sensors are binary valued.
  • Center-mounted tristimulus color sensor.
  • Two center mounted “pens” which, when working in tandem, make a mark five units wide.
s robot commands
S-Robot Commands
  • MOV <arg> -- move <arg> units
  • SWI <arg> -- swivel <arg> degrees
  • SPD <arg> -- set speed <arg> micro-units per time step
  • SNP <arg> -- sense proximity by updating values of the proximity vector
  • SNC <arg> -- sense color by updating values of the color vector
  • PUP <arg> -- pen #<arg> up
  • PDN <arg> -- pen #<arg> down

Notes: Discrete event simulation determines number of time steps needed when trying to complete a move or when trying to complete a swivel.

s robot on board controller
S-Robot “On-Board” Controller
  • Queues a command sequence then sleeps until sequence is executed. Sequences can include “motifs”.

if (sensed red component == target value)

qzigzag(q); /* schedule “zigzag” motif */

q.put(SWI), q.put(-55);

else

q.put(SWI), q.put(20); q.put(PDN), q.put(P1);

q.put(SPD), q.put(750); q.put(MOV), q.put(12);

q.put(SWI), q.put(-10); q.put(PUP), q.put(P1)

q.put(SNC), q.put(0);

evolutionary framework
Evolutionary Framework
  • GOAL: Using two hand-crafted controllers, evolve starting positions (sx, sy), where 0 < sx, sy < L, and initial true compass headings d, where -180 < d < 180, for either TWO or FOUR S-Robots.
  • Grid Side Length: L = 200.
  • Number of time steps: T = 150,000.
  • Population Size: P = 16.
  • Number of Generations: G = 30.
  • Replacement: P/2 individuals using P/4 “breeding pairs”.
  • Recombination: One-point crossover.
  • Mutation: Non-elitest (!) point mutation.
  • Culling Interval: Every I = 5 generations.
assessment parameters
Assessment Parameters
  • Np – the number of squares of the grid that were painted.
  • Nb – the number of times an S-Robot reacted in response to a forward proximity bit set, but rear proximity bit clear.
  • Ns – the number of an S-Robot reacted in response to a forward proximity bit set and rear proximity bit set.
  • Nc – the number of times an S-Robot found a desired color through color sensing.
evolved s robot paintings
Evolved S-Robot Paintings
  • Fitness F = Np + 1000Nb + 100Ns using two Type A controllers which do NOT make use of the SNC color sense command.
slide19
Fitness F = Np, again using two Type A controllers which do NOT make use of the SNC color sense command.
  • Generations 10, 20, and 30:
slide21
Fitness F = Np -100Ns +1000Nc, using two Type A controllers which do NOT make use of the SNC color sense command.
  • Generations 5, 10, and 15:
using type a and b controllers
Using Type A and B Controllers
  • Fitness F = Np-Nb+100Ns+1000Nc.
  • Generations 0, 15, and 20.
slide24
Fitness F = Nb*Nc, which selects for tightly coupled following behavior.
  • Generations 0, 10, and 20.
slide25
Fitness F = Nb*Nc + Np*Ns, which again selects for following behavior but also tries to increase canvas coverage.
  • Generations 5, 15, and 30.
on autonomous evaluation
On Autonomous Evaluation
  • S-Robots maintain a history of their previous starting positions and headings.
  • S-Robots maintain their current Nb, Nc, and Ns values.
  • After T time steps, an elected S-Robot “sweeps” the canvas to calcuate Np.
  • By sharing data, each S-Robot calculates the fitness F.
  • By comparing with previous saved fitness values S-Robots decides which saved genomes to recombine for the next generation.
  • Via a PRNG new genomes are self-generated and S-Robots re-position and re-orient themselves for the next generation’s painting.
conclusions
Conclusions
  • Fitness functions can induce aesthetics for evolved S-Robot paintings.
  • S-Robots can collectively evaluate their own creative efforts.
  • S-Robot behaviors that (indirectly) influence aesthetics can be evolved.
slide28
Thank-you!

http://www.mathcs.richmond.edu/~ggreenfi/

ggreenfi@richmond.edu

…. Questions?