evo06 talk

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.

Leonel Moura: ArtSBot: 

Leonel Moura: ArtSBot

J. McCormack: EvoMUSART ‘05: 

J. McCormack: EvoMUSART ‘05 Open Problem #3: “To create EMA systems that produce art recognized by humans for its artistic contribution (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.

Slide8: 

Monmarche Greenfield

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);

S-Robot Testing…Wall Avoidance: 

S-Robot Testing…Wall Avoidance

…Collisions ...Periodicity: 

…Collisions ...Periodicity

…Pens …Colors …Motifs: 

…Pens …Colors …Motifs

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:

Slide20: 

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.

Slide23: 

Generations 10, 20, and 30 after changing one of the selected motifs.

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?