Automata for learning sequential tasks |
| |
Authors: | C H Ben Choi |
| |
Affiliation: | (1) Department of Electrical Engineering, The Ohio State University, 43210 Columbus, Ohio, USA |
| |
Abstract: | This paper describes a system that is capable of learning both combinational and sequential tasks. The system learns from
sequences of input/output examples in which each pair of input and output represents a step in a task. The system uses finite
state machines as its internal models. This paper proposes a method for inferring finite state machines from examples. New
algorithms are developed for modifying the finite state machines to allow the system to adapt to changes. In addition, new
algorithms are developed to allow the system to handle inconsistent information that may result from noise in the training
examples. The system can handle sequential tasks involving long-term dependencies for which recurrent neural networks have
been shown to be inadequate. Moreover, the learned finite state machines are easy to be implemented in VLSI. The system has
a wide range of applications including but not limited to (a) sequence detection, prediction, and production, (b) intelligent
macro systems that can learn rather than simply record sequences of steps performed by a computer user, and (c) design automation
systems for designing finite state machines or sequential circuits.
C. H. Ben CHOI, Ph.D.: He got his B. S., M. S., and Ph.D. degrees all from The Ohio State University in the United States. His major areas of study
include Solid State Microelectronics, Computer Engineering, and Computer Information Science. He has works on general associative
memory, parallel and distributed computer architectures, and machine learning. He currently works on a project concerning
theoretical aspects of learning machine. His research interests include hardware and software methods of building an intelligent
machine. |
| |
Keywords: | Learning Sequential Tasks Machine Learning Inference Mechanisms Finite State Machines Learning from Examples |
本文献已被 SpringerLink 等数据库收录! |
|