An incremental approach to developing intelligent neural network controllers for robots.

Citation data:

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, ISSN: 1083-4419, Vol: 26, Issue: 3, Page: 474-85

Publication Year:
1996
Usage 10
Abstract Views 10
Captures 1
Readers 1
Citations 44
Citation Indexes 44
Repository URL:
https://works.swarthmore.edu/fac-comp-sci/28
PMID:
18263048
DOI:
10.1109/3477.499797
Author(s):
Meeden, Lisa A.
Publisher(s):
Institute of Electrical and Electronics Engineers (IEEE)
Tags:
Engineering; Computer Science; Artificial Intelligence and Robotics; Computer Sciences
article description
By beginning with simple reactive behaviors and gradually building up to more memory-dependent behaviors, it may be possible for connectionist systems to eventually achieve the level of planning. This paper focuses on an intermediate step in this incremental process, where the appropriate means of providing guidance to adapting controllers is explored. A local and a global method of reinforcement learning are contrasted-a special form of back-propagation and an evolutionary algorithm. These methods are applied to a neural network controller for a simple robot. A number of experiments are described where the presence of explicit goals and the immediacy of reinforcement are varied. These experiments reveal how various types of guidance can affect the final control behavior. The results show that the respective advantages and disadvantages of these two adaptation methods are complementary, suggesting that some hybrid of the two may be the most effective method. Concluding remarks discuss the next incremental steps toward more complex control behaviors.