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Modern Robots: Evolutionary Robotics Jeff Clune Assistant Professor Evolving Artificial Intelligence Laboratory Thursday, February 14, 13 Golem Project Thursday, February 14, 13 Other Generative Encodings • Grammatical Approach - Lindenmayer Systems (L-Systems) - Hornby, Lipson, Pollack 2003 - Cellular Encoding - Gruau ~1992 • Cellular Approach - GRNs - Thursday, February 14, 13 Bongard Development Bongard Thursday, February 14, 13 Development Bongard Thursday, February 14, 13 Grammar-based Generative Representations Lindenmayer systems (L-systems) Thursday, February 14, 13 A= • Rules: A à AB, BàC, Cà_ B= • A C= • AB • ABC • ABC • AAA • AB • ABC • ABC ABC ABC Lindenmayer Systems http://bit.ly/a2D7hQ http://bit.ly/c4IVCi Thursday, February 14, 13 http://bit.ly/a2D7hQ http://bit.ly/9CeplS Lindenmayer Systems Thursday, February 14, 13 http://en.wikipedia.org/wiki/File:Dragon_trees.jpg Genobots: L-system evolved robots Hornby & Pollack 2002, Hornby Lipson & Pollack 2003 Thursday, February 14, 13 Genobots: L-system evolved robots Hornby & Pollack 2002, Hornby Lipson & Pollack 2003 Thursday, February 14, 13 Genobots: L-system evolved robots Hornby & Pollack 2002, Hornby Lipson & Pollack 2003 Thursday, February 14, 13 Genobots: L-system evolved robots Hornby & Pollack 2002, Hornby Lipson & Pollack 2003 Thursday, February 14, 13 Genobots: L-system evolved robots Hornby & Pollack 2002, Hornby Lipson & Pollack 2003 Thursday, February 14, 13 L-Systems • Plusses and Minuses? • Let’s play: http://www.kevs3d.co.uk/dev/lsystems/# Thursday, February 14, 13 Context-Free vs. Context Sensitive • Context-Free: always builds same thing (stubborn) • Context-Sensitive: can respond to environment (reactive) • rules: - IF (temp < 60): A à AB - ELSE: AàC Thursday, February 14, 13 Representations in Other AI Areas • So far, we have discussed representing solutions • How about a objective function? Thursday, February 14, 13 • evaluates the value of a game state • how can you learn it? Explicit vs. Implicit Representations For Objective Function Approximations • Implicit • Thursday, February 14, 13 Upside? Explicit vs. Implicit Representations For Objective Function Approximations • Implicit Upside • Thursday, February 14, 13 - learn faster - generalize to unseen boards (necessary for large state spaces) - low memory requirement Explicit vs. Implicit Representations For Objective Function Approximations • Implicit Upside • • Thursday, February 14, 13 - learn faster - generalize to unseen boards (necessary for large state spaces) - low memory requirement Downside? Explicit vs. Implicit Representations For Objective Function Approximations • Implicit Upside • - learn faster - generalize to unseen boards (necessary for large state spaces) - low memory requirement Downside • - conflates situations - which one? Implicit representations at different abstractions provide a spectrum from Explicit to Highly Implicit • - Thursday, February 14, 13 similar tradeoffs Explicit vs. Implicit Representations For Objective Function Approximations • Explicit: one-to-one mapping (sound familiar?) .... 12% 42% 98% • Implicit: one-to-many mapping (sound familiar?) - 1 piece - 3 piece + 3 piece 33% 2% 85% Thursday, February 14, 13 Evolvability: L-systems vs. CPPNs F=C0FF-[C1-F+F+F]+[C2+F-F-F] Thursday, February 14, 13 F=C0FF-[C1-F+F+F]+[C2+F-F-F]-F CPPNs What would you parameterize? Spotlight casting shadow http://picbreeder.com/search/showgenome.php?sid=395 Thursday, February 14, 13 DNA Tool Thursday, February 14, 13 CPPN Thursday, February 14, 13 Entangled Node Thursday, February 14, 13
