Building grounded abstractions for artificial intelligence programming
Name
49223440-MIT.pdf
Description
Full printable version
Size
4.51 MB
Format
Adobe PDF
Checksum (MD5)
42354794a59e91685f13eaff9cdfe8ef
Author(s)
Hearn, Robert A. (Robert Aubrey), 1965-
Advisor(s)
Gerald J. Sussman.
Date Issued
2001
Publisher
Massachusetts Institute of Technology
Abstract
Most Artificial Intelligence (AI) work can be characterized as either "high-level" (e.g., logical, symbolic) or "low-level" (e.g., connectionist, behavior-based robotics). Each approach suffers from particular drawbacks. High-level Al uses abstractions that often have no relation to the way real, biological brains work. Low-level Al, on the other hand, tends to lack the powerful abstractions that are needed to express complex structures and relationships. I have tried to combine the best features of both approaches, by building a set of programming abstractions defined in terms of simple, biologically plausible components. At the "ground level", I define a primitive, perceptron-like computational unit. I then show how more abstract computational units may be implemented in terms of the primitive units, and show the utility of the abstract units in sample networks. The new units make it possible to build networks using concepts such as long-term memories, short-term memories, and frames. As a demonstration of these abstractions, I have implemented a simulator for "creatures" controlled by a network of abstract units. The creatures exist in a simple 2D world, and exhibit behaviors such as catching mobile prey and sorting colored blocks into matching boxes. This program demonstrates that it is possible to build systems that can interact effectively with a dynamic physical environment, yet use symbolic representations to control aspects of their behavior.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 57-58).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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