1. Using Artificial Life to evolve Artificial Intelligence Virgil Griffith California Institute of Technology http://virgil.gr virgil@caltech.edu Google Tech Talk - 2007

2. 2 What is Artificial Life? Origin of Life Today Life,and might have beenas it is…

3. Evolution: an abbrev intro Evolution is an algorithm Given only: Variable population Selection Reproduction with occasional errors Regardless of substrate, you get evolution!

4. 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

5. [Blocky Creatures Movie]

6. Using Artificial Life to evolve Artificial Intelligence

7. 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 - ?)

8. 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

10. 10 Not to be confused with:

11. 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

12. 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

13. Polyworld Overview Organisms have: evolving genes, and mate sexually a body and metabolism neural network brains initial neural wiring is genetic At birth, all neural weights are random Hebbian learning refines synapse weights throughout lifetime 1-dimensional vision (like Flatland) No fitness function Fitness is determined by natural selection alone Critter Colors Red = current aggression Blue = current horniness

14. [Movie - Sample]

15. Body Genes Size Strength Max speed Max lifespan Fraction of energy given to offspring Greenness Point-mutation rate Number of crossover points

16. 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

17. Polyworldian brain map Random Energy Level Move Turn Eat Mate Fight Light Focus Input Units Processing Units

18. 18 Polyworld Brain Map (actual)

19. 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

20. Behavior sample: Eating

21. Behavior sample: Killing & Eating

22. Behavior sample: Mating

23. Behavior sample: Lighting

24. New Species: Joggers

25. New Species: Indolent Cannibals

26. Emergent Behavior: Visual Response

27. Emergent Behavior: Fleeing Attack

28. Foraging, Grazing, Swarming

29. 29 Observations from Polyworld Evolution generates a wide range brain wirings Selection for use of vision Evolution of emergent behaviors

30. Ideal Free Distribution in agents with evolved neural architectures Early Middle Late

31. 31 Predator-Prey Cycles

32. Cat Polyworldian Random

33. 33 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 self- representation” “A metabolism” “Functional interactions with the environment” “The ability to evolve” Farmer, Belin (1992)

34. 34 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

35. 35 Neural Functional Complexity

36. Is there an evolutionary “arrow of complexity”? Yes – Darwin, Lamarck, Huxley, Valentine No – Lewontin, Levins, Gould Gould (1994) Carroll (2001)

37. 37 Evolution drives complexity?

38. 38 Genetic complexity over time

39. 39 Neural Complexity: Room to grow

40. 40 Future Directions More… measures of complexity complex environment food types agent senses (touch, smell) Behavioral Ecology Optimal foraging (profit vs. predation risk) Evolutionary Biology Speciation = ƒ (population isolation) Altruism = ƒ (genetic similarity) Classical conditioning, animal intelligence experiments

41. 41 Source Code Source code is available! Runs on Mac/Linux (via Qt) http://www.sf.net/projects/polyworld/

42. But is this a good idea?

43. 43 Special Thanks Larry Yaeger Chris Adami

44. Plasticity in Neural Function Mriganka Sur, et al Science 1988, Nature 2001 Function maps The redirect

45. Plasticity in Wiring Patterns of long-range connections in V1, normal A1, and rewired A1 Mriganka Sur, et al. Nature 2001

46. Hebbian Learning: Structure from Randomness John Pearson, Gerald Edelman

47. Real and Artificial Brain Maps Monkey Cortex, Blasdel and SalamaSimulated Cortex, Ralph Linsker Distribution of orientation-selective cells in visual cortex

48. 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 Neuroscience Recap

49. 49 Thanks to Larry Yaeger Chris Adami

50. 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

51. 51 Neural Group Mutual Information

52. 52 Evolution drives max complexity?