1. Frankencritters Greg Reshko and Chris Smoak

2. Background 1989 Larry Yaeger – Apple Computer Polyworld – Artificial Life Software Simulated small creatures that could eat, mate, attack, see, and move 5 - 15 sec./frame Some emergent behavior – showed promise

3. Artificial Life Model and simulate complex biological systems Usually combines multiple traditional AI parts Introduces more biologically-based parts Explore complex systems Life, Tierra, Eden, Polyworld, etc.

4. Goals Continue Polyworld’s intentions Improve performance Improve algorithms and correctness Observe emergent behavior Learn about ALife and complex systems Validate biologically-based complex systems

5. Simulated World Large open space for critters to live in Not too large to encourage interaction Critters 50 – 100 at once Obstacles Plants Long simulation time

7. Critter Design - Physical Simple triangular shape Vision Sensitivity to color Adjustable field of view Movement / Turning ability Eating / Mating / Attacking / Lighting Energy provides life 2 types of energy: stored and ready

8. Critter Design - Mental BCM – like neural network brain Model developed to approximate neurons in the visual cortex Adapt to changing inputs – plasticity Vision and Energy inputs Move / Eat / Attack, etc. outputs Neurons appear in groups 10 – 32 neuron groups and same for neurons in groups Neurons excitatory or inhibitory

10. Critter Design - Evolution Employs standard genetic algorithm No explicit fitness function Fitness evaluated by “passing along your genes” Crossover / Mutation of genes Critter described by its genome ~1460 genes Describe all physical / mental aspects

11. Critter Design - Evolution Physical genes Energy usage rates Base metabolism / Max energy usage Indirectly describe size / strength Mental genes Describe general layout of brain and its interconnections Brain “grown” from these parameters – no two alike

12. Architectural Design Distributed system with multiple cross-platform clients (Windows / Linux / Solaris) Server handles rendering the world and interactions Clients process the neural networks Real-time analysis client IPC network protocol Library by Reid Simmons (CMU/RI) OpenGL rendering (5 – 15 frames/sec.) User display and each critter’s view Movie output (AVI format)

13. Analysis Dumping of individual brains in multiple formats Plaintext (in the future: import brains) HTML (group connectivity overview).GDL (graphical layout) Dumping of critter genome Real-time dumping of various system-wide statistics HTML with JPEGs Num. births / deaths, avg. critter energy, etc.

14. Analysis (cont) Movie output Speeds up visual observation Keeps record of interesting behavior Critter selection / observation Behind-the-shoulder view Eye view Various statistics

15. Lasers Greg got bored and made our simulator a “game” You were the only one to have a weapon It was a laser It was red It killed the other critters Playtesting currently in progress

16. Behaviors Interesting to note tendency of critters to always be turning Caused by the way the turn behavior is expressed Observed behaviors Grazing – critter slows down when near food, eats – multiple observations Prolific mating

17. Future Work Getting all the bugs out More analysis tools Cross-generation genome analysis Longer test-runs Testing fitness Placing existing critter in new environment Mixing separately-evolved populations Increased performance