Jordan Pollack
Location: (Boston, MA)
Personal Research Web Page: http://jordanpollack.com
Keywords: Artificial Life, Evolutionary Computation, Coevolutionary Learning, Machine Learning, Educational technology, Evolutionary Robotics
Posted on: Wednesday, May 25th, 2011
Broad Research Area: AI / Machine Learning / Robotics / Vision
Research Interests:
DEMO focuses on origins and principles of organization for complex systems. If we can create chain reactions in complexity with a universal computational substrate, then certain hard problems should become easier to solve.
Co-Evolutionary Learning
Most learning takes place as optimization of a fixed environment or fitness function. This requires that the learner be “pre-adapted” to that environment in order to learn anything, or that the environment be “gradient engineered” for the particular learning mechanism. This inductive bias usually makes the researcher the primary cause of learning. We focus on dynamically-changing learning environments, often composed of competing learners, where the complexity of the task gradually and automatically increases without human intervention. We work in games, language learning, and computational optimization tasks.
Evolutionary Machines
The long term goal is the co-evolution of machines and their brains, first in simulation, then, through advanced computer-aided manufacturing, into actual hardware. Our initial experiments simulated LEGO structures and evolved groups of simulated robot agents. The GOLEM project through 5 generations evolved designs which could be built into reality using automated manufacture. The frontier is to co-evolve form and formation, to evolve manufacturing plans which can overcome noise and errors.
Contact Information:
email pollack@brandeis.edu Cell 781 266 1400
