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35th Annual Simulation Symposium
Faster than Real-Time Machine Learning within High Fidelity Simulations
San Diego, California
April 14-April 18
ISBN: 0-7695-1552-5
Ethan E. Danahy, Tufts University
Stephen A. Morrison, Tufts University
Imagine using a virtual learning environment to remove the programmer from the process of developing code for mechanical movement. Efficient artificial intelligence combined with a high fidelity simulation would allow the computer to discover valid, optimal actions for a robot in faster than real-time, thus eliminating the need for human guess-and-test. This paper presents the challenges of developing such a system, and describes a robotic machine and associated simulation that gives testimony to this possibility.
Index Terms:
robotics, legged locomotion, virtual reality, simulations, artificial intelligence, machine learning, genetic algorithms, genetic programming, artificial intelligence
Citation:
Ethan E. Danahy, Stephen A. Morrison, "Faster than Real-Time Machine Learning within High Fidelity Simulations," ss, pp.0300, 35th Annual Simulation Symposium, 2002
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