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Multiresolutional schemata for unsupervised learning of autonomous robots for 3D space operationThis paper describes a novel approach to the development of a learning control system for autonomous space robot (ASR) which presents the ASR as a 'baby' -- that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allows it to build this knowledge from the experiences of actions within a particular environment (we will call it an Astro-baby). The learning techniques are rooted in the recursive algorithm for inductive generation of nested schemata molded from processes of early cognitive development in humans. The algorithm extracts data from the environment and by means of correlation and abduction, it creates schemata that are used for control. This system is robust enough to deal with a constantly changing environment because such changes provoke the creation of new schemata by generalizing from experiences, while still maintaining minimal computational complexity, thanks to the system's multiresolutional nature.
Document ID
19940030545
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Lacaze, Alberto (Drexel Univ. Philadelphia, PA, United States)
Meystel, Michael (Drexel Univ. Philadelphia, PA, United States)
Meystel, Alex (Drexel Univ. Philadelphia, PA, United States)
Date Acquired
September 6, 2013
Publication Date
May 1, 1994
Publication Information
Publication: NASA. Goddard Space Flight Center, The 1994 Goddard Conference on Space Applications of Artificial Intelligence
Subject Category
Cybernetics
Accession Number
94N35051
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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