Evolving Intelligence (EI) Project

 


Robert T. Pennock
Professor
Lyman Briggs College
Dept. of Philosophy
Dept. of Computer Science and Engineering
Ecology, Evolutionary Biology and Behavior Program

 The EI project investigates the evolution of intelligence. Using evolving digital organisms in the Alife environment of Avida and other digital evolution systems, we explore patterns in the emergence of simple intelligent behavior and its components, including phenotypic plasticity, complexity, memory, inference, cooperation, and curiosity.  I think of this as experimental epistemology. The abstract below from one of the early grants that funded this project introduces a few of the ideas that motivate the research.

Aristotle defined human beings as the rational animal, identifying our intelligence as the characteristic that differentiated our essential nature from that of other animals. Intelligence is commonly recognized as one of the highest forms of complexity to emerge in biological systems. How did this astounding level of functional organization arise? Was intelligence simply the result of a lucky accident? Did its emergence require fine tuning of multiple historical factors? Or was the emergence of intelligence inevitable or at least likely? Is intelligence really unique to human beings, or might similar mental capacities be found, or implemented, elsewhere?

Stephen Jay Gould in Wonderful Life (1989) argued that intelligent beings like ourselves were an evolutionary fluke, famously suggesting that if one were to rewind the tape of life and start again one should not expect a similar outcome. Others, such as Daniel Dennett in Darwin’s Dangerous Idea (1995) and Simon Conway Morris in Life’s Solution (2003) argue that intelligence is such a useful commodity that evolution could not help but have converged upon it. Religion has long considered the origin of human intelligence to be a spiritual mystery, with some holding that mental capacities belong solely to us by virtue of possession of a soul.

Herbert Simon, the Nobel laureate who is recognized as one of the founders of the field of Artificial Intelligence (AI), saw the human mind as “Wonderful, but not incomprehensible.” In 1956, together with his colleague Allen Newell, Simon designed the first AI system—Logic Theorist—which discovered proofs for theorems in logic. This pioneering research inspired a generation of investigators, who worked to design complex computer systems to mimic human cognition. However, despite some remarkable successes, the creation of intelligent machines has proved to be remarkably difficult and AI research has not fulfilled its early promise.

The PI of this grant (Robert Pennock) was a graduate student of Simon and has suggested that one reason for the difficulties is that Simon’s original empirical work led researchers to begin at the top with human intelligence rather than considering how that could have emerged from simpler forms of intelligence in other beings. Put another way, the problem may be that too much emphasis was placed on Aristotle’s notion of the rational and not enough on the notion of the animal. Rather than focus immediately on higher order propositional intelligence (i.e. knowing that), surely it makes more sense to begin with behavioral intelligence (i.e. knowing how) that is more commonly shared across species. Many of Simon’s key theoretical insights about rationality, such as the importance of pattern recognition and satisficing behavior, can be fruitfully redeployed in this alternative bottom-up evolutionary approach.

In our view, the failure of AI may reflect the difficulty of top-down design of something so complex as high-level intelligence, which all scientific evidence indicates evolved from (was built on a scaffold of) other forms of intelligence that existed before humans evolved. Could it be, then, that the best or only way to AI is not to design it from scratch but, instead, to create circumstances that allow it to evolve? Might there not be general evolutionary principles that would lead to the emergence of such complexity in digital as well as biological environments?

[Excerpt from Project Description]

 

 

 


Current Students and Postdocs

Jory Schossau
Postdoc, Integrative Biology

Jake Cosineau
Undergraduate Professorial Assistant

Former Lab Group Members

Leigh Sheneman
PhD Student, Computer Science

Arend Hintze
Postdoc

Mike Weisenauer
Master's Student

Robin Miller
Undergraduate Professorial Assistant

Frank Bartlett
Postdoc, Zoology

Aaron Wagner
Postdoc, Zoology & Computer Science

David Bryson
PhD Student, Computer Science

Jason Walker
Undergraduate Student, Computer Science

Jesus Rivera
Master's Student, Computer Science

Wesley R. Elsberry
Postdoc

Jeff Clune
PhD Student, Computer Science / Philosophy

Laura Grabowski
PhD Student, Computer Science

 

Faculty Collaborators

Kay Holekamp
University Distinguished Professor
Director of the Ecology, Evolutionary Biology and Behavior Program
Integrative Biology

Arend Hintze
Assistant Professor
Integrative Biology

Richard Lenski
Hannah Distinguised Professor of Microbial Ecology

Risto Mikkulainen
Professor
Computer Science, Univ. of Texas - Austin

Charles Ofria
Professor
Computer Science and Engineering

Fred C. Dyer
Professor
Dept. of Zoology

 

 

Selected Publications

Robert T. Pennock. “Evolution and Computing.” In Jonathan B. Losos & Richard E. Lenski (eds.) How Evolution Shapes Our Lives: Essays on Biology and Society.  Princeton: Princeton University Press. (2016, pp. 206-219)

Laura M. Grabowski, David M. Bryson, Fred C. Dyer, Robert T. Pennock and Charles Ofria. “A Case Study in the De Novo Evolution of a Complex Odometric Behavior in Digital Organisms.” PLoS ONE (2013; 8(4):e60466)

Jeff Clune, Robert T. Pennock, Charles Ofria and Richard Lenski. “Ontogeny tends to recapitulate phylogeny in digital organisms.” The American Naturalist, Vol. 180, No. 3 (July 2012, E54-E63)

Laura M. Grabowski, David M. Bryson, Fred C. Dyer, Robert T. Pennock and Charles Ofria. “Clever Creatures: Case Studies of Evolved Digital Organisms.” Proceedings of the European Conference on Artificial Life 11 (August 2011)

Jeff Clune, Kenneth O. Stanley, Robert T. Pennock, and Charles Ofria. “On the Performance of Indirect Encoding Across the Continuum of Regularity.” IEEE Transactions on Evolutionary Computation, Vol. 15, No. 3, pp. 346-367.  (June 2011)

Jeff Clune, Heather Goldsby, Charles Ofria, Robert T. Pennock. “Selective pressures for accurate altruism targeting: Evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory.” Proceedings of the Royal Society B (Vol. 278, pp. 666-674, doi:10.1098/rspb.2010.1557. Published online: 15 September, 2010)

Laura M. Grabowski, David M. Bryson, Fred C. Dyer, Charles Ofria, Robert T. Pennock. “Early Evolution of Memory Usage in Digital Organisms.” Proceedings of the International Conference on Artificial Life (ALife XII) (August 2010)

Jeff Clune, Benjamin E. Beckmann, Robert T. Pennock and Charles Ofria.  “HybrID: A Hybridization of Indirect and Direct Encodings for Evolutionary Computation.” Proceedings of the 2009 Evolutionary Conference of Artificial Life (ECAL).  Springer.  (2010)

Jeff Clune, Charles Ofria, and Robert T. Pennock. “The Sensitivity of HyperNEAT to Different Geometric Representations of a Problem.” Proceedings of the 2009 Genetic and Evolutionary Computation Conference (GECCO ‘09) Montreal, Canada. New York: Association for Computing Machinery (2009, pp. 675-682).

Jeff Clune, Benjamin E. Beckmann, Charles Ofria, and Robert T. Pennock.  “Evolving coordinated quadruped gaits using the HyperNEAT generative encoding.”  Proceedings of IEEE on Evolutionary Computing Special Section on Evolutionary Robotics.  Trondheim, Norway. (2009, pp. 2764-2771) [GECCO best paper award winner]

Wesley R. Elsberry, Laura M. Grabowski, Charles Ofria, and Robert T. Pennock.  “Cockroaches, Drunkards, and Climbers: Modeling the Evolution of Simple Movement Strategies Using Digital Organisms.” Proceedings of IEEE Symposium on Artificial Life (ALIFE 2009) Symposium Series on Computational Intelligence. (2009, pp. 92-99)

Jeff Clune, Charles Ofria and Robert T. Pennock.  “How A Generative Encoding Fares as Problem-Regularity Decreases.” In G. Rudolph et al. (eds.). Parallel Problem Solving in Nature.  Berlin: Springer-Verlag(2008, pp. 358-367).

Jeff Clune, Charles Ofria and Robert T. Pennock.  “How Generative Encodings Fare on Less Regular Problems.” Genetic and Evolutionary Computation Conference (GECCO ‘08) Proceedings. New York: Association for Computing Machinery (2008, pp. 867-868)

Laura Grabowski, Wesley Elsberry, Charles Ofria and Robert T. Pennock.  “On the Evolution of Motility and Intelligent Tactic Response.” Genetic and Evolutionary Computation Conference (GECCO ‘08) Proceedings. New York: Association for Computing Machinery (2008, pp. 209-216)

Jeff Clune, Charles Ofria, Robert T. Pennock.  “Investigating the Emergence of Phenotypic Plasticity in Evolving Digital Organisms” In Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I. and Coutinho, A., Advances in Artificial Life.  Berlin: Springer.  (2007, pp. 74-83).

Robert T. Pennock. "Models, Simulations, Instantiations and Evidence: The Case of Digital Evolution" Journal of Experimental and Theoretical Artificial Intelligence (2007, Vol. 19, No. 1, pp. 29-42)

Sherri Goings, Jeff Clune, Charles Ofria, Robert T. Pennock. "Kin-Selection: The Rise and Fall of Kin-Cheaters."  In Pollack, Jordan, M. Bedau, P. Husbands, T. Ikegami and R. Watson. (eds.) Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems. (2004, pp. 303-308)

Richard Lenski, Charles Ofria, Robert T. Pennock, Christoph Adami. "The Evolutionary Origin of Complex Features."  Nature.  (2003, Vol. 423. 8 May, pp. 139-144)

Robert T. Pennock. "Can Darwinian Mechanisms Make Novel Discoveries?:  Learning from discoveries made by evolving neural networks." Foundations of Science  Vol. 5 no. 2, pp. 225-238, 2000.

An evolving population of digital organisms.


Created 1/15/2006. Last updated 4/11/2019.
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