Electrical Engineering and Computer Science

AI Seminar

An Agent-Based Model of Language Acquisition and Evolution

Prof John H. Holland

Tuesday, October 11, 2005
4:00pm - 5:30pm
175 ATL (Large Conference Room)

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Snacks provided.

About the Event

The contemplation in natural science of a wider domain than the actual leads to a far better understanding of the actual. – A. S. Eddington Is it possible to model language acquisition and evolution using simple cognitive mechanisms not tied to language? The model proposed here involves the interaction of multiple agents trying to acquire spatially distributed resources in order to survive and reproduce. In the parlance of linguistics – the agents are situated. Language has value to the agents only if it increases their ability to collect resources. The model centers on inter-agent communication that combines elements of a limited vocabulary to describe novel or complex situations. In linguistic terms – the agents are structured. The model is constrained by well-established concepts from cognitive psychology, but it does not employ parameters from language acquisition studies. That is, the model is exploratory: It is intended to outline possibilities, not actualities. Because the model can be run with and without language acquisition capabilities, control experiments are possible: Each agent’s survival depends upon its ability to collect resources that are distributed spatially in its environment. Language has value to the agent only if it increases the agent’s ability to collect these resources. There’s no explicit a priori value assigned to language. Control experiments are possible: The model can be run with and without language acquisition. Note: This is an exploratory model. The model is meant to demonstrate that simple cognitive mechanisms not tied to language are adequate to the acquisition and evolution of grammars. The model is constrained by well-established concepts from cognitive psychology, but it does not employ parameters based on language acquisition studies.


John H. Holland, known worldwide as the father of genetic algorithms is one of today's most innovative and visionary thinkers in the emerging science of complexity. Holland is Professor of Electrical Engineering and Computer Science, and Professor of Psychology at the University of Michigan. He received his B.S. in Physics from MIT in 1950, and his M.A. in Math and Ph.D. in Communication Science from Michigan in 1954 and 1959 respectively. Currently Holland is Co-Chairman of the Science Board of the Santa Fe Institute and a member of the Executive Committee, Board of Trustees and External Professor for the Institute. He is Director of the University of Michigan/Santa Fe Institute Research Program. Holland is a member of the Executive Committee, Program for Complex Systems, and an Associate of the Institute for Humanities both at the University of Michigan. Holland has also received numerous awards and honors including the Levy Medal of the Franklin Institute, the Henry Russel Lectureship from the University of Michigan (the highest honor the University can confer upon a member of the faculty), the World Economic Counsel Fellowship and the MacArthur Fellowship. Holland's most recent book, Emergence: from Chaos to Order, was published in 1998. He also wrote Hidden Order: How Adaptation Builds Complexity (1995), Induction: Processes of Inference, Learning and Discovery (with K.J. Holyoak, R.E. Nisbett and P.R. Thagard) (1986), Adaptation in Natural and Artificial Systems (1975). Holland also holds a patent with A.W. Burks (no. 4,697,242) Adaptive Computing System Capable of Learning and Discovery. Holland also is a member of many editorial boards: Machine Learning, Complex Systems, Adaptive Behavior, Evolutionary Computation, and Complexity.

Additional Information

Contact: Bob Marinier

Email: rmarinie@umich.edu

Sponsor(s): AI Lab

Open to: Public