1 / 18

Frankencritters

Frankencritters. Greg Reshko and Chris Smoak. 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. Artificial Life.

harton
Download Presentation

Frankencritters

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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)

  11. 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.

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

  13. 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

  14. 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

  15. 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

More Related