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Massachusetts Institute of Technology

Building grounded abstractions for artificial intelligence programming

Abstract

dc:description.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.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2001

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Hearn, Robert A. (Robert Aubrey), 1965-
Advisor dc:contributor.advisor
  • Gerald J. Sussman.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/17510
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/17510

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Hearn, Robert A. (Robert Aubrey), 1965-. Building grounded abstractions for artificial intelligence programming. Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/17510