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Using Artificial Life to evolve Artificial Intelligence Virgil Griffith California Institute of Technology http://virgil.gr [email protected] Google Tech Talk - 2007 What is Artificial Life? Life, as it is… and might have been Origin of Life Today 2 Evolution: an abbrev intro Evolution is an algorithm Given only: Variable population Selection Reproduction with occasional errors Regardless of substrate, you get evolution! Forming body plans with evolution Node specifies part type, joint, and range of movement Edges specify the joints between parts Population? Graphs of nodes and edges Selection? Ability to perform some task (walking, jumping, etc.) Mutation? Node types change/new nodes grafted on [Blocky Creatures Movie] Using Artificial Life to evolve Artificial Intelligence How to model Intelligence? Marionettes (ancient Greeks) Hydraulics (Descartes) Pulleys and gears (Industrial Revolution) Telephone switchboard (1930’s) Boolean logic (1940’s) Digital computer (1960’s) Neural networks (1980’s - ?) Nervous Systems Evolution found and stuck with nervous systems across all levels of complexity Provide all behaviors—including anything that might be considered intelligence—in all organisms more complex than plants Some behaviors are innate, so the wiring diagram (the connections) must matter But some behaviors are learned, so learning— phenotypic plasticity—must also matter Polyworld Not to be confused with: QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. 10 What Polyworld is Making artificial intelligence the way Nature made natural intelligence: The evolution of nervous systems in an ecology Working our way up the intelligence spectrum Research tool for evolutionary biology, behavioral ecology, cognitive science What Polyworld is not Fully open ended Accurate model of microbiology Accurate model of any particular ecology though could be done Accurate model of any animal’s brain though could be done Polyworld Overview Organisms have: evolving genes, and mate sexually a body and metabolism neural network brains 1-dimensional vision (like Flatland) No fitness function initial neural wiring is genetic At birth, all neural weights are random Hebbian learning refines synapse weights throughout lifetime Fitness is determined by natural selection alone Critter Colors Red = current aggression Blue = current horniness QuickTime™ and a H.264 decompressor [Movie - Sample] are needed to see this picture. Body Genes Size Strength Max speed Max lifespan Fraction of energy given to offspring Greenness Point-mutation rate Number of crossover points Brain Genes Vision # of neurons for seeing red # of neurons for seeing green # of neurons for seeing blue # of internal neural groups For each neural group… # of excitatory neurons # of inhibitory neurons Initial bias of neurons Bias learning rate For each pair of neural groups… Connection density for excitatory neurons Connection density for inhibitory neurons Learning rate for excitatory neurons Learning rate for inhibitory neurons Polyworldian brain map Move Turn Eat Mate Fight QuickTime™ and a TIFF (Uncomp resse d) de com press or are nee ded to s ee this picture. Light Energy Level Focus Random Input Units Processing Units Polyworld Brain Map (actual) 18 All about Energy (Health) Get Energy by: eating food pellets eating other Polyworldians Lose Energy by: mating, moving, existing having large size or strength but get benefits in max-energy and fighting brain activity for computational reasons and parsimonious brain size Behavior sample: Eating QuickTime™ and a H.264 decompressor are needed to see this picture. Behavior sample: Killing & Eating QuickTime™ and a H.264 decompressor are needed to see this picture. Behavior sample: Mating QuickTime™ and a H.264 decompressor are needed to see this picture. Behavior sample: Lighting QuickTime™ and a H.264 decompressor are needed to see this picture. New Species: Joggers QuickTime™ and a H.264 decompressor are needed to see this picture. New Species: Indolent Cannibals QuickTime™ and a H.264 decompressor are needed to see this picture. Emergent Behavior: Visual Response QuickTime™ and a H.264 decompressor are needed to see this picture. Emergent Behavior: Fleeing Attack QuickTime™ and a H.264 decompressor are needed to see this picture. Foraging, Grazing, Swarming QuickTime™ and a H.264 decompressor are needed to see this picture. Observations from Polyworld Evolution generates a wide range brain wirings Selection for use of vision Evolution of emergent behaviors 29 Early Ideal Free Distribution in agents with evolved neural architectures Middle Late Predator-Prey Cycles 31 Qu ickTim e™ a nd a TIFF (Un comp resse d) decompresso r are need ed to see th is picture. Qu ickTim e™ a nd a TIFF (Un comp resse d) decompresso r are need ed to see th is picture. Cat Random Qu ickTim e™ a nd a TIFF (Un comp resse d) decompresso r are need ed to see th is picture. Polyworldian But is it Alive? Ask Farmer & Belin… “Life is a pattern in space-time, rather than a specific material object” “Self-reproduction” “Information storage of a selfrepresentation” “A metabolism” “Functional interactions with the environment” “The ability to evolve” Farmer, Belin (1992) 33 But is it Intelligent? No obvious way to measure intelligence (aka: We don’t know) even biologists have a hard time on this But we’re in a simulation, that means we can use techniques not available to biology! Information theory Complexity theory 34 Neural Functional Complexity 35 Is there an evolutionary “arrow of complexity”? Yes – Darwin, Lamarck, Huxley, Valentine No – Lewontin, Levins, Gould Carroll (2001) Gould (1994) Evolution drives complexity? 37 Genetic complexity over time 38 Neural Complexity: Room to grow 39 Future Directions More… Behavioral Ecology Optimal foraging (profit vs. predation risk) Evolutionary Biology measures of complexity complex environment food types agent senses (touch, smell) Speciation = ƒ (population isolation) Altruism = ƒ (genetic similarity) Classical conditioning, animal intelligence experiments 40 Source Code Source code is available! Runs on Mac/Linux (via Qt) http://www.sf.net/projects/polyworld/ 41 But is this a good idea? Special Thanks Larry Yaeger Chris Adami 43 Plasticity in Neural Function The redirect Mriganka Sur, et al Science 1988, Nature 2001 Function maps Plasticity in Wiring Patterns of long-range connections in V1, normal A1, and rewired A1 Mriganka Sur, et al. Nature 2001 Hebbian Learning: Structure from Randomness John Pearson, Gerald Edelman Real and Artificial Brain Maps Distribution of orientation-selective cells in visual cortex Monkey Cortex, Blasdel and Salama Simulated Cortex, Ralph Linsker Neuroscience Recap Intelligence is based in brains Useful brain functions are created by a: suitable initial neural wiring general purpose learning mechanism Artificial neural networks capture key features of biological neural networks Thus, we could make useful artificial neural systems with: An evolving population of wiring diagrams Hebbian learning Thanks to Larry Yaeger Chris Adami 49 What can Evolution do? Optimization Traffic Lights Air Foil Shape Fuzzy Problems Sonar response from sunken ships versus live submarines Good for management tasks, such as timetables and resource scheduling Even good for evolving learning algorithms and simulated organisms and behaviors Neural Group Mutual Information 51 Evolution drives max complexity? 52