Intrinsic representation : bootstrapping symbols from experience
Name
57124903-MIT.pdf
Description
Full printable version
Size
8.51 MB
Format
Adobe PDF
Checksum (MD5)
fec8c07175c2867162d5a04da9a66b0a
Author(s)
Larson, Stephen David, 1980-
Advisor(s)
Patrick H. Winston.
Date Issued
2003
Publisher
Massachusetts Institute of Technology
Abstract
If we are to understand human-level intelligence, we need to understand how meanings can be learned without explicit instruction. I take a step toward that understanding by focusing on the symbol-grounding problem, showing how symbols can emerge from a system that looks for regularity in the experiences of its visual and proprioceptive sensory systems. More specifically, my implemented system builds descriptions up from low-level perceptual information and, without supervision, discovers regularities in that information. Then, my system, with supervision, associates the regularity with symbolic tags. Experiments conducted with the implementation shows that it successfully learns symbols corresponding to blocks in a simple 2D blocks world, and learns to associate the position of its eye with the position of its arm. In the course of this work, I take a new perspective on how to design knowledge representations, one that grapples with the internal semantics of systems, and I propose a model of an adaptive knowledge representation scheme that is intrinsic to the model and not parasitic on meanings captured in some external system, such as the head of a human investigator.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 95-96).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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