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Full-Text Articles in Engineering
An Emotional Mimicking Humanoid Biped Robot And Its Quantum Control Based On The Constraint Satisfaction Model, Quay Williams, Scott Bogner, Michael Kelley, Carolina Castillo, Martin Lukac, Dong Hwa Kim, Jeff S. Allen, Mathias I. Sunardi, Sazzad Hossain, Marek Perkowski
An Emotional Mimicking Humanoid Biped Robot And Its Quantum Control Based On The Constraint Satisfaction Model, Quay Williams, Scott Bogner, Michael Kelley, Carolina Castillo, Martin Lukac, Dong Hwa Kim, Jeff S. Allen, Mathias I. Sunardi, Sazzad Hossain, Marek Perkowski
Electrical and Computer Engineering Faculty Publications and Presentations
The paper presents a humanoid robot that responds to human gestures seen by a camera. The behavior of the robot can be completely deterministic as specified by a Finite State Machine that maps the sensor signals to the effector signals. This model is further extended to the constraints-satisfaction based model that links robots vision, motion, emotional behavior and planning. One way of implementing this model is to use adiabatic quantum computer which quadratically speeds-up every constraint problem and will be thus necessary to solve large problems of this type. We propose to use the remotely-connected Orion system by DWAVE Corporation.
Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson
Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson
Electrical and Computer Engineering Faculty Publications and Presentations
"Learning Hardware" approach involves creating a computational network based on feedback from the environment (for instance, positive and negative examples from the trainer), and realizing this network in an array of Field Programmable Gate Arrays (FPGAs). Computational networks can be built based on incremental supervised learning (Neural Net training) or global construction (Decision Tree design). Here we advocate the approach to Learning Hardware based on Constructive Induction methods of Machine Learning (ML) using multivalued functions. This is contrasted with the Evolvable Hardware (EHW) approach in which learning/evolution is based on the genetic algorithm only.