Artificial Life and Evolving Intelligence Laura M. Grabowski, Ph.D. Department of Computer Science The University of Texas-Pan American

Alife and Evolution Avida Evolving Intelligence Conclusion Intro to Artificial Life What is Life?

Alife and Evolution Conclusion Intro to Artificial Life What is Artificial Life? ArtificialLife Avida Evolving Intelligence

Situating Artificial Life (Alife) in Computer Science Alife and Evolution Conclusion Artificial Intelligence Automated Reasoning Computer Vision... Natural Language Understanding Machine Learning Evolutionary Computation Artificial Life Digital Evolution Intro to Artificial Life Avida Evolving Intelligence

AI Run Amok! Or, Gratuitous Pop Culture References Alife and Evolution Conclusion Intro to Artificial Life Avida Evolving Intelligence

Evolutionary Computation Subfield of Artificial Intelligence (AI) Methods apply principles of Darwinian evolution to problem- solving EC methods can produce patentable, human-competitive solutions EC systems contain One or more populations of individuals Competition for resources Alife and Evolution Conclusion Intro to Artificial Life Image source: Avida Evolving Intelligence

EC Overview Genetic algorithm Iterative search Individuals are encodings of candidate solutions Optimization problems Genetic programming Individuals are computer programs Digital evolution Evolutionary robotics Conclusion Evolved satellite antenna design Image source: ToGA_Tutorial-goodman.pdf Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

What is Alife? Common thread: understanding the “general prinicples that govern the living state” 2 Broad range of disciplines Computer science Engineering Biochemistry Physics … and more … Alife and Evolution Conclusion Intro to Artificial Life 2 Adami, C. (1998). Introduction to Artificial Life. Telos, Santa Clara, CA, p. 34. Avida Evolving Intelligence

Alife and Evolution Conclusion Intro to Artificial Life “What makes living systems alive?” 1 How do we approach this question? Life is complex and diverse Deconstructing living system (analysis)? Constructing artificial living systems (synthesis)! Image source: 1 Adami, C. (1998). Introduction to Artificial Life. Telos, Santa Clara, CA, p. vii. Avida Evolving Intelligence

AlifeThemes Construct life in artificial medium Compare with terrestrial life Desire to learn about living world (emulation, simulation) Alife may give results that falsify theories about life Alife may provide novel or improved solutions to engineering problems Alife and Evolution Conclusion Intro to Artificial Life Image source: virtual-creatures.html Image source: papers/ngarden.htm Avida Evolving Intelligence

Alife and Evolution Conclusion Intro to Artificial Life Alife and Evolution Alife is a part of the field of Evolutionary Computation Alife approaches often leverage evolutionary processes Avida Evolving Intelligence

Conclusion Fundamental Principles of Evolution* Replication Descent with modification Competition *D. C. Dennett. (2002). The new replicators. In M. Pagel, editor, Encyclopedia of Evolution. Oxford University Press, New York, E83- E92. Charles Darwin, age 31 Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Conclusion Brief Highlights from Alife Work Karl Sims Creatures, 1994 Creatures Hod Lipson, self- copying robots Risto Mikkulainen’s group, 2008 Big Dog robot Big Dog Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Digital Evolution Population of self- replicating “digital organisms” Organisms replicate, mutate, compete Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

The Avida* Digital Evolution Platform Virtual environment (“vitrual Petri dish), but real evolution** Used for research in biology and computer science * C. Ofria and C. O. Wilke. (2004). Avida: a software platform for research in computational evolutionary biology. In Artificial Life 10, ** Pennock, R.T. (2007). Models, simulations, instantiations, and evidence: the case of digital evolution. Journal of Experimental and Theoretical Artificial Intelligence, 19(1): Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Avida Overview Figure from: Lenski, R. E., Ofria, C., Pennock, R.T., & Adami, C. (2003). The evolutionary origin of complex features. Nature, 423, Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Top-down vs. bottom-up approaches Goals Behavioral flexibility Emergence “Intelligence” is not one big ability, but many smaller ones Building blocks Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Building Blocks of Intelligence Common threads Differential behavior based on current environment Using past experience Requires such capabilities as Sensing Memory Decision-making Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Evolving Motility and Taxis Using Avida Inspired by chemical gradient-following behavior of E. coli Evolution of chemotaxis-like response* * Grabowski, L.M., Elsberry, W.R., Ofria, C., and Pennock, R.T. (2008). On the evolution of motility and intelligent tactic response. GECCO '08: Proceedings of the 10 th Annual Conference on Genetic and Evolutionary Computation, pp Purpose: Proof-of-concept, evolving simple navigation Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Experimental Environment Organisms evolved to traverse idealized gradient Two treatments  Implicit memory: provided prior information  Without implicit memory: did not provide prior information Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Representative Trajectory Plots of Evolved Organisms Conclusion Implicit Memory 26/100 replicate populations* Evolved Memory 7/100 replicate populations* *Closest approach within 10% of initial distance to peak Organism Trajectory Peak Location Initial Location Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Conclusions Proof-of-concept for motility in Avida Evolution of fundamental navigation Tactic behavior easier to evolve when memory is provided Key result: Evolution of rudimentary memory mechanism Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Memory: Critical Component of Intelligence Memory is a hurdle in evolving intelligent behavior. Conclusion Primary hippocampal neuron Image source: Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Evolving Memory Use Bees: An example of behavioral intelligence Variety of navigation strategies Show use of different memory capabilities Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Path-following Experiments in Avida Path-following experiments with honey bees * Similar path environment in Avida User-defined set of environmental “cues” Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence *Zhang, S.W., Bartsch, K. & Srinivasan, M. V. (1996). Maze learning by honeybees. Neurobiology of Learning and Memory, 66:

Evolving Short-Term Memory Conclusion Intermittent updating of information “New” turn direction cued by specific right or left cue Other turns have different cue, repeat- last Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Results: Paths Experienced During Evolution Conclusion Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Results: Novel Path Conclusion Grabowski, L. M., Bryson, D. M., Pennock, R. T., Dyer, F. C., & Ofria, C. (2010). Early evolution of memory use in digital organisms. Proceedings of the 12 th International Conference on the Synthesis and Simulation of Living Systems (ALife XII). MIT Press, pp Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Conclusions Conclusion Memory and flexible behavior evolve in even simple environments. Evolution capitalizes on both environmental change and environmental regularity to construct solutions. Evolved memory mechanisms use both organization of genome and volatile states in the CPU. Alife and Evolution Intro to Artificial Life Avida Evolving Intelligence

Future Directions Rudimentar y Memory Precursors to Memory One-bit Memory Conclusion Introduction Evolving Navigation Behaviors  Path Integration  Landmark Navigation Evolution of complexity Transfer from Avida to robotic platforms Global domination

Thank you! For more information: Laura M. Grabowski’s Homepage Avida Digital Life Platform Michigan State University Digital Evolution Laboratory Michigan State University Evolving Intelligence Project l