By Topic

The psychology of robots

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Schmajuk, N.A. ; Dept. of Psychol., Duke Univ., Durham, NC, USA

In recent years, neural networks have been proposed that portray many of the complexities of adaptive behavior. The networks describe how agents learn to predict future events by: 1) building models of the would, 2) inferring new predictions from past experiences, 3) combining elementary environmental stimuli into complex internal representations, 4) attending to stimuli associated with environmental novelty, and 5) attending to stimuli that are good predictors of other environmental events. When a predictive network is attached to a goal seeking system, the resulting architecture is able to describe spatial and maze navigation, as well as problem solving and planning. When the predictions of future events are based on the combination of environmental stimuli and the animal's own responses the networks provide the information necessary to choose between alternative behaviors. When the agent's own responses can be identified with the responses of other agents, the networks can describe learning by imitation. It is suggested that these principles might be applied to the design of adaptive, communicating autonomous robots

Published in:

Proceedings of the IEEE  (Volume:84 ,  Issue: 10 )